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Introduction
Security Requirements
Security Policies and Models
Practical Security Engineering
- Model Engineering
  - Model Family
- Model Specification
Security Mechanisms
Security Architectures

Introduction

Risk Scenarios

Reducing Operational Risks: What responsibilities can we bear?

Critical Properties

Security + Safety
Reliability
Correctness
Availability
Real Time
Scalability
Openness

Examples:

Information Systems
Communication Systems
Energy Grid Management
Traffic Management
Consumer Technology

Responsibility for risks -> guaranteed properties!

Relevance of Security: Security properties if any IT system are mission-critial - independet of its application domain

Security Goals

Our Faculty's Education and Examination Management System

Maintains:
- Course profiles (examination form/date, credit points)
- Students records (personal data, registration to examinations, grades)
Services:
- Enrolment/expulsion of students
- Registration to examination
- Registration of examination marks
- Information and attestations desk
Operational Risks
- Conditio sine qua non: Provability of information properties
- Fake registration to examinations: integrity, non-repudiability ("nicht-abstreitbar")
- Leakage of grades, personal data: confidentiality, integrity
- Forgery of attestations: authenticity, integrity

Industry Control Systems

e.g. Factorys, energy and water plants (public infrastructure)
- "Chinese Hacking Team Caught Takin over decoy water plant"
- "Internet Attack shuts off the Heat in Finland"
Operational risks: Integrity & Availability of public community support systems

Self Study Task Read about these two scenarios. Find one or more recent examples for attacks on public infrastructure, including some technical details, in the news. Keep all these scenarios in mind, we will come back to them in the next chapter:

Message

Goal of IT Security: Reduction of Operational Risks of IT Systems
Elementary: Protection of
- Confidentiality
- Integrity
- Availability
- Non-repudiability

Specific Security Goals (Terms)

Confidentiality: the property of information to be available only to anauthorized user group
Integrity: the property of information to be protected against unauthorized modification
Availability: the property of information to be available in an reasonable time frame
Authenticity: the property to be able to identify the author of an information
Non-repudiability: the combination of integrity and authenticity

	Safety	Security
Goal	To protect environment against hazards caused by system failures	To protect IT systems against hazards caused by malicious attacks
	Technical failures: power failure, ageing, dirt	Industrial espionage, fraud, blackmailing
	Human errors: stupidity, lacking education, carelessness	Terrorism, vandalism
	Force majeure: fire, lightning, earth quakes
=> making sure things work	in the presence of system failures	in the face of an intelligent and maliciousadversary

Security Engineering

Security Goals in Practice

... are diverse and complex to achieve
... require multiple stakeholders to cooperate
... involve cross-domain expertise

Security Engineering:

Is a methodology that tries to tackle this complexity.
Goal: Engineering IT systems that are secure by design.
Approach: Stepwise increase of guarantees -> formal methods required!

Steps in Security Engineering:

Lecture Roadmap

Security Requirements: Vulnerabilites, Threats, Risks
Security Policies and Models: Access Control, Information Flow, Non-Interference
Practical Security Engineering: Model Engineering, Model, Specification, Model Implementation
Security Mechanisms: FYI Authorization, Authentication, Cryptography
Security Architectures: TCBs and Reference Monitors, Nizza,SELinux, Kerberos

Security Requirements

Motivation

Goal of Requirements Engineering:

Methodology for

identifying
specifying

the desired security properties of an IT system.

Result:

Security requirements, which definewhatsecurity properties a system should have.
These again are the basis of asecurity policy: Defineshowthese properties are achieved

Influencing Factors

Codes and acts (depending on applicable law)
- EU General Data Protection Regulation (GDPR)
- US Sarbanes-Oxley Act (SarbOx)
Contracts with customers
Certification
- For information security management systems (ISO 27001)
- Subject to German Digital Signature Act (Signaturgesetz), toCommon
Criteria
Company-specific guidelines and regulations
- Access to critical data
- Permission assignment
Company-specific infrastructure and technical requirements
- System architecture
- Application systems (such as OSs, Database Information Systems)

General Methodology: How to Come up with Security Requirements

Specialized steps in regular software requirements engineering:

Identify and classifyvulnerabilities.
Identify and classifythreats.
Match both, where relevant, to yieldrisks.
Analyze and decide which risks should bedealt with. -> Fine-grained Security Requirements

Vulnerability Analysis

Goal: Identification of

technical
organizational
human vulnerabilities of IT systems.

Vulnerability

Feature of hardware and software constituting, an organization running, or a human operating an IT system, which is a necessary precondition for any attack in that system, with the goal to compromise one of its security properties. Set of all vulnerabilities = a system’sattack surface.

Human Vulnerabilities

Examples:

Laziness
- Passwords on Post-It
- Fast-clicking exercise: Windows UAC pop-up boxes
Social Engineering
- Pressure from your boss
- A favor for your friend
- Blackmailing: The poisoned daughter, ...
- An important-seeming email
Lack of knowledge
- Importing and executing malware
- Indirect, hidden information flowin access control systems

Social Engineering

Influencing people into acting against their own interest or the interest of an organisation is often a simpler solution than resorting to malware or hacking. Both law enforcement and the financial industry indicate that social engineering continues to enable attackers who lack the technical skills, motivation to use them or the resources to purchase or hire them. Additionally, targeted social engineering allows those technically gifted to orchestrate blended attacks bypassing both human and hardware or software lines of defence. Europol

Real Cases

Self Study Task

Investigate the following real-world media (all linked from moodle). Find any potential security vulnerabilities there and give advice how to avoid them.

Watch (no listening required) the interview with staff of the French TV station TV5Monde

Read the lifehacker article about Windows UAC

Read the Washington Times article about Facebook Scam

Read the two eMails I received lately.

Indirect Information Flow in Access Control Systems

A More Detailed Scenario

AlphaCompany has two departments: Research & Development(R&D) and Sales
Ann is project manager and Bob is developer working in R&D on ProjectX, Chris is a busybody sales manager writing a marketing flyer about ProjectX
All R&D developers communicate via an electronic bulletin board, including any preliminary product features not yet ready for release
Bob is responsible for informing sales about release-ready features, using ashared web document

Security Requirement

No internal information about a project, which is not approved by the project manager, should ever go into the product flyer.

Access Control Configuration

3 users:ann,bob,chris
2 groups:
- crewx: ann, bob, ...
- sales: ann, bob

Settings:

drw- --- --- 1 ann crewx 2020-04-14 15:10 ProjectXFiles
-rw- r-- --- 1 ann crewx 2020-04-14 15:10 ProjectXBoard
-rw- r-- --- 1 bob sales 2020-04-14 14:22 NotesToSales
-rw- --- --- 1 chris sales 2020-04-13 23:58 SalesFlyer.pdf

Result:
- all users apparently set their permissions perfectly - from their own point of view
- all three together createda severe information flow vulnerability...
Ann has read access to the folder ProjectX Files
Ann legitimately writes news from these files to the ProjectX Board
Bob legitimately updates NotesToSales with these news
Human vulnerability: Bob’s laziness, friendship with Chris, blackmailing by Chris, ... (see above) make him write about unapproved new features
-> Chris misuses this information in the Sales Flyer...

Forbidden Information Flow

Internal information about ProjectX goes into the product flyer!

Problem Analysis:

Limited knowledge ofusers
- limited horizon: knowledge about the rest of a system configuration for making a sound decision about permissions
- limited problem awareness: see "lack of knowledge"
- limited skills
Problem complexity -> effects ofindividualpermission assignments by users (= discretionary) tosystem-widesecurity properties
Limited configuration options and granularity: archaic and inapt security mechanisms in system and application software
- no isolation of non-trusted software
- no enforcement of global security policies
-> Effectiveness of discretionary access control (DAC), configured by users?

Organizational Vulnerabilities

Examples:

Access to rooms (servers!)
Assignment of permission on organizational level, e. g.
- 4-eyes principle
- need-to-know principle
- definition of roles and hierarchies
Management of cryptographic keys
- -> e. g. for issuing certificates
-> Master course on "IT-Sicherheitsmanagement" (in German)

Technical Vulnerabilities

The Problem: Complexity of IT Systems

... will in foreseeable timenotbe
Completely, consistently, unambiguously, correctly specified
- -> contain specification errors
Correctly implemented
- -> contain programming errors
Re-designed on a daily basis(many security mechanisms of today’s systems are older than 40 years)
- -> contain conceptual weaknesses and vulnerabilities

Buffer Overflow Attacks

Example for Exploitation of Implementation Errors

in privileged system software:

Operating Systems (OSs)
SSH demons
Web servers
Database servers

Consequence: Privileged software can be tricked into executing attacker’s code

Approach: Cleverly forged parameters overwrite procedure activation frames in memory

-> exploitation of missing length checks on input buffers
-> buffer overflow

What an Attacker Needs to Know

Necessary Knowledge and Skills

Source code of the target program (e. g. a privileged server), obtained by disassembling
Better: symbol table, as with an executable not stripped from debugging information
Even better: most precise knowledge about the compiler used w.r.t. runtime management
- how call conventions affect the stack layout
- degree to which stack layout is deterministic, which eases experimentation

Sketch of the Attack Approach (Observations during program execution)

Stack grows towards the small addresses
- -> small whenever a procedure is called, all its information is stored in aprocedure frame = subsequent addresses below those of previously stored procedure frames
in each procedure frame: address of the next instruction to call after the current procedure returns (ReturnIP)
after storing the ReturnIP, compilers reserve stack space for local variables -> these occupy lower addresses

Preparing the Attack

Attacker carefully prepares an input argument msg:\0 ...\0 /bin/shell#system

void processSomeMsg(char *msg, int msgSize){ 
  char localBuffer[1024];
  int i=0;
  while (i<msgSize) {
    localBuffer[i] = msg[i];
    i++;
  }
  ...
}

Result:

Attacker made victim program overwrite runtime-critical parts of its stack:
- by counting up to the length of msg
- at the same time writing back over previously save runtime information -> ReturnIP
After finishing processSomeMsg: victim program executes code at address of ReturnIP =address of a forged call to execute arbitrary programs!
Additional parameter to this call: file system location of a shell

Security Breach

The attacker can remotely communicate, upload, download, and execute anything- with cooperation of the OS, since all of this runs with the original privileges of the victim program!

Self-Study Task

Do an internet research: Find a (more or less) recent example of a successful buffer overflow attack. Describe as precise as possible what happened during the attack and which programming error made it possible! Commonplace search engines for news articles, but also web databases of software vulnerabilities such as https://cve.mitre.org/ may help you.

Summary

Vulnerabilities

Human
- Laziness
- Social engineering
- Lack of knowledge (e. g. malware execution, DAC shortcoming)
Organizational
- Key management
- Physical access to rooms, hardware
Technical
- Weak security paradigms
- Specification and implementation errors
-> A whole zoo of vulnerabilities!

How can we identify all during systems design and engineering...?

Vulnerabilities catalogues: ISO 27001, ISO 27002
Vulnerabilities databases, such as CVE
Tools (we will see...)

Threat Analysis

Goal: Identification of

Attack objectives and attackers
Attack methods and practices (a.k.a. "Tactics, Techniques, and Procedures (TTPs)")
-> know your enemy

Approach: Compilation of a threat catalog, content:

identified attack objectives
identified potential attackers
identified attack methods & techniques
damage potential of attacks

Attack Objectives and Attackers

Attack Objectives

Economical and political power
Profit
Wreak havoc (energy infrastructure, water plants, air traffic ...)
Meet a challenge

Attackers

Professional organizations (which may be hired by anyone, incl. competitors or governments)
Active and former employees ("Remember that IT guy we fired last year ...?")
Terrorists
Hackers (both good or evil)

Examples

Economic Espionage
- Objective: economic and political power, profit
- Victims: high tech industry(companies that rely on the secrecy of their know-how to successfully compete)
- Attackers:
  - Competitors, (foreign) governments -> professional organizations
  - Insiders
    - regular, often privileged users of IT systems
    - statistically large share(> 40 %)
    - often indirect -> social engineering ("Only amateurs target systems; professional target people.")
    - statistical profile: age 30-40, executive function (department heads, system administrators, lead programmers, ...)
    - weapons: technical and organisational insider knowledge, technical skills
    - -> Your own people.
Personal Profit
- Objective: becoming rich(er)(expensive life style, ambitious projects, medical conditions)
- Attackers:
  - Competitors
  - Insiders
    - profile: age 40-50, management function
    - typically: career peak reached, midlife crisis, new boat, new house, new partner, ...
    - weapons: organisational insider knowledge, organisational authority, management and leadership skills
Wreak Havoc
- Objective: damaging or destroying things or lives, blackmailing, meeting a challenge (egomania, narcissism, sportive challange)
- Attackers:
  - Terrorists: motivated by faith and philosophy, paid by organisations and governments
  - Avengers: see insiders
  - Psychos: all ages, all types, personality disorder (egomania, narcissism, paranoia, ...)
  - -> No regular access to IT systems, no insider knowledge, butskills and tools.

Attack Methods

Exploitation of Vulnerabilities

Human: Social engineering, laziness, lack of knowledge
Organizational: Rights management, key management, room access
Technical: Weak protection paradigms, specification and implementation errors

Examples

Scenario 1: Insider Attack

Social Engineering, plus
Exploitation of conceptual vulnerabilities (DAC),plus
Professionally tailored malware

Scenario 2: Malware(a family heirloom ...)

Trojan horses: Executable code with hidden functionality.
Viruses: Code for self-modification and self-duplication, often coupled with damaging the host.
Logical bombs: Code that is activated by some event recognizable from the host (e. g. time, date, temperature, pressure, geographic location, ...).
Backdoors: Code that is activated through undocumented interfaces (mostly remote).
Ransomware: Code for encrypting possibly all user data found on the host, used for blackmailing the victims (to pay for decryption).
Worms and worm segments: Autonomous, self-duplicating programs. Originally designed for good: to make use of free computing power in local networks.

Scenario 3: Outsider Attack

Attack Method: Buffer Overflow
Exploitation of implementation errors

Scenario 4: High-end Malware:Root Kits

Goal: Invisible, total, sustainable takeover of a complete IT system
Method: Comprehensive tool kit for fully automated attacks
1. automatic analysis of technical vulnerabilities
2. automated attack execution
3. automated installation of backdoors
4. automated installation and activation of stealth mechanisms
Target: Attacks on all levels of the software stack:
- firmware
- bootloader
- operating system (e. g. drivers, file system, network interface)
- system applications (e. g. file and process managers)
- user applications (e. g. web servers, email, office)
tailored to specific software and software versions found there!

Self-Study Task

Read about the following malware examples, both historical and up to date. Identify each as a virus, logical bomb, backdoor, ransomware, or worm; think about which necessary vulnerabilit(y|ies) make all of these threats so dangerous.

One of the most sophisticated pieces of malware ever discovered:Stuxnet

One of the first large-scale malware threats in history:Michelangelo

Two cooperating pieces of malware that put at risk numerous public institutional IT systems:Emotet and Ryuk.

Root Kits

Step 1: Vulnerability Analysis

Tools look for vulnerabilities in
- Active privileged services and demons (from inside a network:nmap, from outside: by port scans) -> Discovers:web server, remote access server (sshd), file server (ftpd), time server (ntpd), print server (cupsd),bluetoothd,smbd, ...
- Configuration files -> Discovers: weak passwords, open ports
- Operating systems -> Discovers: kernel and system tool versions with known implementation errors
Using built-in knowledge base: an automatable vulnerability database
Result: System-specific collection of vulnerabilities -> choice of attack method andtools to execute

Step 2: Attack Execution

Fabrication oftailored softwareto exploit vulnerabilities in
- Server processes or system tool processes (demons)
- OS kernel itself to execute code of attacker withroot privileges
This code
- First installs smoke-bombs for obscuring attack
- Then replaces original system software by pre-fabricated modules
  - servers and demons
  - utilities and libraries
  - OS modules
- containing
  - backdoors (-> step 3)
  - smoke bombs for future attacks (-> step 4)
Results:
- Backdoors allow forhigh-privilege access within fractions of seconds
- System modified with attacker’s servers, demons, utilities, OS modules
- Obfuscation of modifications and future access

Step 3: Attack Sustainability

Backdoors for any further control & command in
- Servers (e. g.sshdemon)
- Utilities (e. g.login)
- Libraries (e. g.PAM, pluggable authentication modules)
- OS (system calls used by programs likesudo)
Modificationsof utilities and OS to prevent
- Killing root kit processes and connections (kill,signal)
- Removal of root kit files (rm,unlink)
Results: Unnoticed access for attacker
- Anytime
- Highly privileged
- Extremely fast
- Virtually unpreventable

Step 4: Stealth Mechanisms (Smoke Bombs)

Clean logfiles (entries for root kit processes, network connections), e.g. syslog,kern.log,user.log,daemon.log,auth.log, ...
Modify system admin utilities
- Process management(hide running root kit processes), e.g. ps,top,ksysguard,taskman
- File system (hide root kit files), e.g. ls,explorer,finder
- Network (hide active root kit connections), e.g. netstat,ifconfig,ipconfig,iwconfig
Substitute OS kernel modules and drivers (hide root kit processes, files, network connections), e.g. /proc/...,stat,fstat,pstat
Result:Processes, files and communication of root kit become invisible

Risk and Damage Potential:

Likeliness of success: extremely highin today’s commodity OSs
- High number of vulnerabilities
- Speed
- Refined methodology
- Fully automated
Fighting the dark arts:extremely difficult
- Number and cause of vulnerabilities
- number of "security updates" last month?
- specification/implementation errors, weak security mechanisms
- Speed
- Smoke bombs
Prospects for recovering the system after successful attack:near zero

Countermeasures - Options:

Reactive: Well ...(even your OS might have become your enemy)
Preventive:
- Counter with same tools for vulnerability analysis (we do this for years now -> 50 Billions € damage taken...)
- Write correct software (we try this for years now -> 50 Billions € damage taken...)

Security Engineering

New paradigms:policy-controlled systems -> powerful software platforms

New provable guarantees: formal security models -> reducing specification errors and faultsby design

New security architectures -> limiting bad effectsof implementation errors and faults

Damage Potential

Industrial Espionage:

Loss of control over critical knowledge -> loss of economical or political power(high-risk technologies!)
Economical damage (contract penalties, loss of profit, image damage) Quantity: 50 000 000 000 €, 40%caused by IT⁶

Personal Profit: Individual loss of money(zero sum game)

Terrorism, hackers:

Loss of critical infrastructures (energy, water, communication)
Loss of sea, air, land transport infrastructure
Damage of financial systems

Summary

Know Your Enemy

Attack goals and attackers
- Economical and political power, financial gain
- Professional organizations, insiders
Attack methods und techniques: exploiting vulnerabilities
- human
- organizational
- technical
-> A zoo of threats, practical assistance:
- National (Germany): BSI IT-Grundschutz standards and catalogues
- International:Common Criteria

Attacks on Public Infrastructure revisited:

Self-Study Task

Take a close look at those example scenarios for attacks on public infrastructure you read and researched about in chapter 1. For all of them, try to answer the following questions:

Who was the presumed attacker mentioned in the article? Classify them according to what you learned about attacker types.

What was the attack objective? Again, classify based on what you learned in this chapter.

How was the attack made possible? Identify the types of vulnerabilities exploited.

Risk Analysis

Goal: Identification and Classification of scenario-specific risks when designing an IT system

Approach:

Risks \subseteq Vulnerabilities \times Threats
Correlation of vulnerabilities and matching threats
- -> Risk catalogue
Classification of risks
- -> Complexity reduction
-> Risk matrix

Correlation of Vulnerabilities and Threats

Goal: Risk catalogue: n:m correlation

Examples

Vulnerability: Implementation error in database access control -> Contents can be accessed by unauthorized users
Threat: Professional team of attackers, contracted by competitor
-> Risk: Confidentiality breach
Vulnerability: Conceptual vulnerability: discretionary access control configuration only
Threat: Employee in critical financial situation
-> Risk:
- Disclosure and sale of corporate secrets
- Redirection of funds
n Vulnerabilities
m Threats
-> x Risks

Usually: $max(n,m)≪ x \leq nm$ -> quite largerisk catalogue!

Risk Classification

Goal: Catalogue reduction -> major and minor risks

Approach: Qualitative risk matrix; dimensions:

Risk Matrix

Damage Potential Assessment

Examples for risks:

Cloud computing:"Loss of VM integrity" -> contract penalties, loss of confidence/reputation
Industrial plant control:"Tampering with frequency converters" -> damage or destruction of facility
Critical public infrastructure:"Loss of availability due to DoS attacks" -> interrupted services, possible impact on public safety (cf. Finnish heating plant)
Traffic management:"Loss of GPS data integrity" -> maximum credible accident w. r. t. safety

General Fact: Damage potential is highly scenario-specific

Example: "Confidentiality breach of database contents"

Articles in online newspapers
- -> small to mediumdamage due to lost paywall revenues
Account data of banks
- -> mission-criticalloss of trust
Plant control data of industrial production facility
- -> mission-criticalloss of market leadership

Depends on diverse, mostly non-technical side conditions

-> advisory board needed for assessment:engineers, managers, users, ...

Occurrence Probability Assessment

Examples for risks:

Cloud computing:"Loss of VM integrity"
- -> depending on client data sensitivity
Industrial plant control:"Tampering with frequency converters"
- -> depending on plant sensitivity(cf.Stuxnet: nuclear centifuges)
Critical public infrastructure:"Loss of availability due to DoS attacks"
- -> depending on terroristic threat level
Traffic management:"Loss of GPS data integrity"
- -> depending on terroristic threat level

General Fact: Occurrence probability ishighly scenario-specific

Example: "Confidentiality breach of database contents"

Articles in online newspapers
- -> smallfor articles that are publicly available anyway
Account data of banks
- -> medium, due to high attack costs compared to potential gain
Plant control data of industrial production facility
- -> high, due to high financial or political gain

Depends on diverse, mostly non-technical side conditions

-> advisory board needed for assessment:engineers, managers, users, ...

Advisory Board Output Example

Object	Risk	Dmg. Pot.	Rationale
Personal Data (PD)	Loss of Confidentiality	medium	(1) Data protection acts, (2) Violation of personal rights
	Loss of Integrity	low	Errors fast and easily detectable and correctable
	Loss of Availability	low	Failures up to one week can be tolerated by manual procedures
Technical Control Data (TCD)	Loss of Confidentiality	high	Loss of market leadership
	Loss of Integrity	high	Production downtime
	Loss of Availability	low	Minimal production delay, since backups are available

Object	Risk	Dmg. Pot.	Rationale
Personal Data (PD)	Loss of Confidentiality	medium	Certified software
	Loss of Integrity	low	Certified software, small incentive
	Loss of Availability	medium	Certified software
Technical Control Data (TCD)	Loss of Confidentiality	high	Huge financial gain by competitors
	Loss of Integrity	medium	Medium gain by competitors or terroristic attackers
	Loss of Availability	low	Small gain by competitors or terroristic attackers

Resulting Risk Matrix

Identify 3 Regions

Form Risks to Security Requirements

avoid: Intolerable risk, no reasonable proportionality of costs and benefits
- -> Don’t implement such functionality!
bear: Acceptable risk
- -> Reduce economical damage, e. g. by insurance.
deal with: Risks that yieldsecurity requirements
- -> Prevent or control by system-enforced security policies.

Additional Criteria:

Again, non-technical side conditions may apply:
- Expenses for human resources and IT
- Feasibility from organizational and technological viewpoints
-> Cost-benefit ratio:management and business experts involved

Security Policies and Models

Security Policies

Motivation - A Traditional Scenario:

Similarity to systems security:protecting valued assets from threats (human life, cargo, ship)
Difference: thousands of years of experience
\rightarrow We may learn something here!
What Protects these Assets?
- Navigation lights:protect against collisions
- Cannons/Guns:protect against pirates
- Reefs, drift anchors:protect against bad weather
- \rightarrow Security Mechanisms
  - Watch:protect against collisions
  - The art of sailing, regulations:protect against & comply with special marine conditions(climate, traffic, canal navigation rules)
- \rightarrow Competent & coordinated operation of mechanisms
- \rightarrow Security Policies
  - Construction of hull
  - Placement of security mechanisms(nav lights in hold)
- \rightarrow Effectiveness of mechanisms and enforcement of security policies
- \rightarrow Security Architecture

Terminology

Security Policies: A Preliminary Definition

We have risks:
- Gales \rightarrow ship capsizes, pirates \rightarrow ship captured
- Malware attack \rightarrow violation of confidentiality and integrity of patient’s medical records
We infer security requirements:
- Protect against gale force 12
- Valid information flows
We design a security policy:
- Rules for dealing with storms, pirates
- Rules for controlling information flows

Security Policy

A set of rules designed to meet a set of security objectives.

Security Objective

A statement of intent to counter a given threat or to enforce a given security policy. (Common Criteria for Information Technology Security Evaluation, since 1996)

Policy representations:

informal (natural language) text
formal model
functional software specification
executable code

Example 1: Excerpt from the Unix Security Policy

\exists subjects(humans, processes) and objects(files, sockets, ...)
Each object has an owner
Owners control access permissions for their objects (\rightarrow DAC)
\exists 3 permissions: read, write, execute
\forall objects: specific permissions can be granted for 3 subject classes: owner, group, others
Example: - rw- r-- r-- 1 peter vsbs 2020-04-19 23:59 syssec-03.pdf
Result:
- \rightarrow identity based + discretionary access control (IBAC + DAC)
- \rightarrow high degree of individual freedom
- \rightarrow global responsibility, limited individual horizon (see 2.1.1)

Example 2: Excerpt from the AlphaCompany Security Policy

Authentication:
1. Each user must be identified based on key certificates issued by Airbus
Authorization: 2. Access to ProjectX files is granted only to the project staff (role-based access control) 3. Changes to files are allowed only if both, the responsible engineer as well as the project leader, approve ("four eyes principle") 4. No information must flow from ProjectX to sales department
Communication: 5. For protecting integrity, confidentiality and authenticity, every communication is encrypted and digitally signed.

How to Implement Security Policies - Some Previews

A Integrated insystems software
- Operating systems
- Database systems
- Middleware platforms
B Integrated inapplication systems

Implementation Alternative A

The security policy is handled anOS abstractionon its own \rightarrow implemented inside the kernel

Policy Enforcement in SELinux

Security Server: Policy runtime environment (protected in kernel space)
Interceptors:Total control of critical interactions
Policy Compiler: Translates human-readable policy modules in kernel-readable binary modules
Security Server: Manages and evaluates these modules

Implementation Alternative B

Application-embedded Policy: The security policy is only known and enforced by oneuser program \rightarrow implemented in a user-space application

Application-level Security Architecture: The security policy is known and enforced by several collaborating user programs in anapplication systems \rightarrow implemented in a local, user-space security architecture

Policy Server Embedded in Middleware: The security policy is communicated and enforced by several collaborating user programs in adistributed application systems \rightarrow implemented in a distributed, user-space security architecture

Security Models

Why We Use Formal Models

Self-Study Task

Please read each of the following scenarios. Then select the statement you intuitively think is most likely:

Linda is a young student with a vision. She fights against environmental pollution and volunteers in an NGO for climate protection. After finishing her studies ...

... she becomes an attorney for tax law.

... she becomes an attorney for tax law, but in her spare time consults environmental activists and NGOs.

Suppose during the 2022 football world cup, Germany reaches the knockout phase after easily winning any game in the previous group phase.

Germany wins the semi-final.

Germany wins the world cup. Think twice about your choices. Can you imagine what other people chose, and why?

Goal of Formal Security Models

Complete, unambiguous representation of security policies for

analyzing and explaining its behavior:

\rightarrow "This security policy will never allow that ..."
\rightarrow "This security policy authorizes/denies an access under conditions ... because ..."

enabling its correct implementation:

\rightarrow "This rule is enforced by a C++ method ..."

How We Use Formal Models: Model-based Methodology

Abstraction from (usually too complex) reality \rightarrow get rid of insignificant details e. g.: allows statements about computability and computation complexity
Precisionin describing what is significant \rightarrow Model analysis and implementation

Security Model

A security model is a precise, generally formal representation of a security policy.

Model Spectrum

Models for access control policies:
- identity-based access control (IBAC)
- role-based access control (RBAC)
- attribute-based access control (ABAC)
Models for information flow policies
- \rightarrow multilevel security(MLS)
Models for non-interference/domain isolation policies
- \rightarrow non-interference(NI)
In Practice: Most oftenhybrid models

Access Control Models

Formal representations of permissions to execute operations on objects, e. g.:

Reading files
Issuing payments
Controlling industrial centrifuges Security policies describeaccess rules \rightarrow security models formalize them

Taxonomy

Identity-based access control models (IBAC)

Rules based on the identity of individual subjects (users, apps, processes, ...) or objects (files, directories, database tables, ...) \rightarrow "Ann may read ProjectX Files."

Role-based access control models (RBAC)

Rules based on roles of subjects in an organization \rightarrow "Ward physicians may modify electronic patient records (EPRs) in their ward."

Attribute-based access control models (ABAC)

Rules based on attributes of subjects and objects \rightarrow "PEGI 18 rated movies may only be streamed to users aged 18 and over."

Discretionary Access Control (DAC)

Individual users specify access rules to objects within their area of responsibility ("at their discretion").

Example: Access control in many OS (e. g. Unix(oids), Windows)

Consequence: Individual users

enjoy freedom w. r. t. granting access permissions as individually needed
need to collectively enforce their organization’s security policy:
- competency problem
- responsibility problem
- malware problem

Mandatory Access Control (MAC)

System designers and administrators specify system-wide rules, that apply for all users and cannot be sidestepped.

Examples:

Organizational: airport security check
Technical: medical information systems, policy-controlled operating systems(e. g. SELinux)

Consequence:

Limited individual freedom
Enforced by central instance:
- clearly identified
- competent (security experts)
- responsible (organizationally & legally)

DAC vs. MAC

In Real-world Scenarios: Mostly hybrid models enforced by both discretionary and mandatory components, e. g.:

DAC: locally within a project, team members individually define permissions w. r. t. documents (implemented in project management software and workstation OSs) inside this closed scope;
MAC:globally for the organization, such that e. g. only documents approved for release by organizational policy rules (implemented in servers and their communication middleware) may be accessed from outside a project’s scope.

Identity-based Access Control Models (IBAC)

Goal: To precisely specify the rights ofindividual, acting entities.

Basic IBAC Paradigm

User named s reads file named o
Client s transfers money to bank account o
Process with ID s sends over socket with ID o

There are

Subjects, i. e. active and identifiable entities, that execute
operations on
passive and identifiable objects, requiring
rights (also: permissions, privileges) which
- control (restrict) execution of operations,
- are checked against identity of subjects and objects.

Access Control Functions [Lampson, 1974]

A really basic model to define access rights:
- Who (subject) is allowed to do what (operation) on which object
- Fundamental to OS access control since 1965 (Multics OS)
- Formal paradigms: sets and functions
Access Control Function (ACF)
- f:S \times O \times OP \rightarrow \{true,false\} where
- S is a set of subjects (e. g. users, processes),
- O is a set of objects(e. g. files, sockets, EPRs),
- OP is a finite set of operations(e. g. reading, writing, deleting).
Interpretation: Rights to execute operations are modeled by the ACF:
- any s\in S represents an authenticated active entity (e. g. a user or process) which potentially executes operations on objects
- any o\in O represents an authenticated passive entity (e. g. a file or a database table) on which operations are executed
- for any s\in S,$o\in O$,$op\in OP$:s is allowed to execute op on o iff f(s,o,op)=true.
- Model making: finding a tuple\langleS,O,OP,f\rangle
  - \rightarrow Definition of S,O, and OP
  - \rightarrow Definition of f

iff = "if and only if"

Example: Implementation of f in a Unix OS (heavily simplified):

S: set of identifiers for users who execute processes
O: set of identifiers for system objects, e. g. files, directories, sockets, ...
OP: set of system call identifiers

Example for f(caller,file,read):

read ( caller , file ) {
  if !(caller.uid == 0) {/* is caller == root? */
    if !(R_MODE in file.inode.othersRWX) {/* check "other"-rights */
      if !(caller.gid == file.inode.group && R_MODE in file.inode.groupRWX) {/* check "group"-rights */
        if !(caller.uid == file.inode.owner && R_MODE in file.inode.ownerRWX) {/* check "group"-rights */
          return ERR_ACCESS_DENIED;/* insufficient rights: deny access */
} } }
/* execute syscall "read" */
}

Self-Study Task

Have a look at your (or any) operating system’s API documentation. Find a few examples for

operations executable by user processes (e. g. Linuxsyscalls),

their arguments,

the operating systems resources they work with. Try to distinguish between subjects, objects and operations as defined in the classical ACF model. Can you see any ambiguities or inconsistencies w. r. t. the model? If you have never worked with an OS API, stick to the simple things (such as reading/writing a file). For Linux, you may find help insection 2 of the online manpages.a ahttp://man7.org/linux/man-pages/dir_section_2.html

Access Control Matrix

Access Control Functions in Practice Lampson [1974] already addresses the questions how to ...

store in a well-structured way,
efficiently evaluate, and
completely analyze an ACF:

Access Control Matrix (ACM)

An ACM is a matrix m:S\times O \rightarrow 2^{OP}, such that \forall s\in S,\forall o\in O:op\in m(s,o)\Leftrightarrow f(s,o,op).

An ACM is a rewriting of the definition of an ACF: nothing is added, nothing is left out ("$\Leftrightarrow$"). Despite a purely theoretical model: paved the way for practically implementing AC meta-informationas

tables
2-dimensional lists
distributed arrays and lists

Example

S=\{s_1 ,...,s_n\}
O=\{o_1 ,...,o_k\}
OP=\{read,write\}
$2^{OP}={\varnothing,{read},{write},{read,write}}^2$

Implementation Notes

ACMs are implemented in most
- Operating systems
- Database information systems
- Middleware platforms(CORBA, Jini/Apache River, Web Services)
- Distributed security architectures (Kerberos)
whose security mechanisms use one of two implementations:

Access Control Lists (ACLs)

Columns of the ACM: char*o3[N] = { "-", "-", "rw", ...};
Found in I-Nodes of Unix(oids), Windows, Mac OS

Capability Lists

Rows of the ACM: char* s1[K] = { "-", "r", "-", ...};
Found in distributed OSs, middleware, Kerberos

What we Actually Model:

Protection State

A fixed-time snapshot of all active entities, passive entities, and any meta-information used for making access decisions is called theprotection state of an access control system.

Goal of ACFs/ACMs

To precisely specify a protection state of an AC system.

The Harrison-Ruzzo-Ullman Model (HRU)

Our HIS scenario ... modeled by an ACM:

S=\{cox, kelso, carla,...\}
O=\{patId, diag, medic,...\}

m	parId	diag	medic	...
cox	{read, write}	{read, write}	{read, write}	...
kelso	{read}	{read}	{read}	...
carla	{read}	`\varnothing`	{read}	...
...

We might do it like this, but ... Privilege escalation question: "Can it ever happen that in a given state, some specific subject obtains a specific permission?" \varnothing \Rightarrow \{r,w\}

ACM models a single state \langleS,O,OP,m\rangle
ACM does not tell us anything about what might happen in the future
Behavior prediction \rightarrow proliferation of rights \rightarrow HRU safety

Why "safety", not "security"? Well, historical ...

We need a model which allows statements about

Dynamic behavior of right assignments
Complexity of such an analysis

Idea [Harrison et al., 1976]: A (more complex) security model combining

Lampson’s ACM \rightarrow for modeling single protection state (snapshots) of an AC system
Deterministic automata (state machines) \rightarrow for modeling runtime changes of a protection state

This idea was pretty awesome. We need to understand automata, since from then on they were used for most security models. \rightarrow Small excursus

Deterministic Automata

Mealy Automaton: \langleQ,\sum,\Omega,\delta,\lambda,q_0\rangle

Q is a finite set of states (state space), e. g. Q=\{q_0 ,q_1 ,q_2\}
\sum is a finite set of input words (input alphabet), e. g. \sum=\{a,b\}
\Omega is a finite set of output words (output alphabet), e. g. \Omega=\{yes,no\}
\delta:Q\times\sum\rightarrow Q is the state transition function
\lambda:Q\times\sum\rightarrow\Omega is the output function
q_0\in Q is the initial state
\delta(q,\sigma)=q′ and \lambda(q,\sigma)=\omega can be expressed through thestate diagram: a directed graph \langleQ,E\rangle, where each edge e\in E is represented by a state transition’s predecessor node q, its successor node q′, and a string "$\sigma|\omega$" of its input and output, respectively.

Example: Return "yes" for any input in an unbroken sequence of "a" or "b", "no" otherwise.

Self-Study Task

I bought a new bluetooth headset for giving this lecture. It has just one button and an LED, and this is what the manual says:

Turn the device on by holding the button for 1 sec.

Turn the device off at any time by holding for 5 sec.

Turn the device on in bluetooth pairing mode by holding for 5 sec.

Switch to pairing mode when on, but not in a phone call, by holding for 1 sec.

Leave pairing mode by clicking the button.

Click to start a call. While in a call, click to hang up.

While on, the LED indicates modes: red during a call, yellow during pairing, green otherwise. Define a mealy automaton for using this headset by defining Q,\sum,\Omega,and q_0 and draw the state diagram for \sigma and \lambda.

HRU Security Model

How we use Deterministic Automata

Snapshot of an ACMis the automaton’s state
Changes of the ACMduring system usage are modeled by state transitions of the automaton
Effects ofoperationsthat cause such transitions are described by the state transition function
Analyses ofright proliferation(\rightarrow privilege escalation)are enabled by state reachability analysis methods

An HRU model is a deterministic automaton \langleQ,\sum,\delta,q_0 ,R\rangle where

Q= 2^S\times 2^O\times M is the state space where
- S is a (not necessarily finite) set of subjects,
- O is a (not necessarily finite) set of objects,
- M=\{m|m:S\times O\rightarrow 2^R\} is a (not necessarily finite) set of possible ACMs,
\sum=OP\times X is the (finite) input alphabet where
- OP is a set of operations,
- X=(S\cup O)^k is a set of k-dimensional vectors of arguments (subjects or objects) of these operations,
\sigma:Q\times\sum\rightarrow Q is the state transition function,
q_0\in Q is the initial state,
R is a (finite) set of access rights.

Interpretation

Each q=S_q,O_q,m_q\in Q models a system’s protection state:
- current subjects set S_q\subseteq S
- current objects set O_q\subseteq O
- current ACM m_q\in M where m_q:S_q\times O_q\rightarrow 2^R
State transitions modeled by \delta based on
- the current automaton state
- an input word \langleop,(x_1,...,x_k)\rangle\in\sum where op
  - may modify S_q (create a user x_i, kill a process x_i etc.),
  - may modify O_q (create/delete a file x_i, open a socket x_i etc.),
  - may modify the contents of a matrix cell m_q(x_i,x_j) (enter or remove rights) where 1\leq i,j\leq k.
- \rightarrow We also call \delta the state transition scheme (STS) of a model.
- Historically: "authorization scheme" [Harrison et al., 1976].

State Transition Scheme (STS)

Using the STS, \sigma:Q\times\sum\rightarrow Q is defined by a set of specifications in the normalized form $\sigma(q,\langleop,(x_1,...,x_k)\rangle)$=if r_1\in m_q(x_{s1},x_{o1}) \wedge ... \wedge r_m\in m_q(x_{sm},x_{om}) then p_1\circle ...\circle p_n where

q=\{S_q,O_q,m_q\}\in Q,op\in OP
r_1 ...r_m\in R
x_{s1},...,x_{sm}\in S_q and x_{o1},...,x_{om}\in O_q where s_i and o_i, 1\leq i\leq m, are vector indices of the input arguments: 1\leq s_i,o_i\leqk
p_1,...,p_n are HRU primitives
Note: \circle is the (transitive) function composition operator: (f\circle g)(x)=g(f(x))

Whenever q is obvious or irrelevant, we use a programming-style notation

Interpretation: The structure of STS definitions is fixed in HRU:

"if": A conjunction of condition clauses (or just conditions) with the sole semantics "is some right in some matrix cell".
"then": A concatenation (sequential execution) of HRU primitives.

Conditions: Expressions that need to evaluate "true" for state q as a necessary precondition for command op to be executable (= can be successfully called).

Primitives: Short, formal macros that describe differences between q and a successor state q′=\sigma(q,\langleop,(x_1 ,...,x_k)\rangle) that result from a complete execution of op:

enter r into m(x_s,x_o)
delete r from m(x_s,x_o)
create subject x_s
create object x_o
destroy subject x_s
destroy object x_o
\rightarrow Each of these with the intuitive semantics for manipulating S_q, O_q or m_q.

Note the atomic semantics: the HRU model assumes that each command successfully called is always completely executed!

How to Design an HRU Security Model:

Model Sets: Subjects, objects, operations, rights \rightarrow define the basic sets S,O,OP,R
STS: Semantics of operations (e. g. the future API of the system to model) that modify the protection state \rightarrow define \sigma using the normalized form/programming syntax of the STS
Initialization: Define a well-known initial stateq 0 =\langleS_0 ,O_0 ,m_0 \rangle of the system to model

An Open University Information System

Informal security policy (heavily simplified):2 rules
- "A sample solution for home assignments can be downloaded by students only after submitting their own solution."
  - a condition for readSample
  - a effect of writeSolution
- "Student solutions can be submitted only before downloading any sample solution."
  - a condition for writeSolution
  - a effect of readSample

Model Making

Sets

Subjects, objects, operations, rights:
- Subjects: An unlimited number of possible students: S\congruent\mathbb{N} (S is isomorphic to N)
- Objects: An unlimited number of possible solutions: O\congruent\mathbb{N}
- Operations:
  - (a) Submit own solution: writeSolution(s_{student},o_{solution})
  - (b) Download sample solution: readSample(s_{student},o_{sample})
  - \rightarrow OP=\{writeSolution, readSample\}
- Rights: Exactly one right allows to execute each operation: R\congruent OP
  - \rightarrow R=\{write, read\}

State Transition Scheme

Effects of operations on protection state:
- writeSolution Informal Policy: "A sample solution (...) can be downloaded by students only after submitting their own solution." \Leftrightarrow "If the automaton receives an input \langlewriteSolution,(s,o)\rangle and the conditions are satisfied, it transitions to a state where s is allowed to download the sample solution."
```
command writeSolution(s,o) ::= if write $\in$ m(s,o) 
  then 
    enter read into m(s,o);
  fi
```
- readSample Informal Policy: "Student solutions can be submitted only before downloading any sample solution." \Leftrightarrow "If the automaton receives an input\langlereadSample,(s,o)\rangleand the conditions are satisfied, it transitions to a state wheresis denied to submit a solution."
```
command readSample(s,o) ::= if read$\in$ m(s,o)
  then
    delete write from m(s,o);
  fi
```

Initialization

By model definition: q_0 =\langleS_0 ,O_0 ,m_0 \rangle
For a course with (initially) three students:
- S_0 =\{sAnn, sBob, sChris\}
- O_0 =\{oAnn, oBob, oChris\}
- m_0:
  - m_0(sAnn,oAnn)=\{write\}
  - m_0(sBob,oBob)=\{write\}
  - m_0(sChris,oChris)=\{write\}
  - m_0(s,o)=\varnothing \Leftrightarrow s\not= o
- Interpretation: "There is a course with three students, each of whom has their own workspace to which she is allowed to submit (write) a solution."

Model Behavior

Initial Protection State

m oAnn oBob oChris

sAnn {write} \varnothing \varnothing

sBob \varnothing {write} \varnothing

sChris \varnothing \varnothing {write}
After writeSolution(sChris, oChris)

m oAnn oBob oChris

sAnn {write} \varnothing \varnothing

sBob \varnothing {write} \varnothing

sChris \varnothing \varnothing {write, read}
After readSample(sChris, oChris)

m oAnn oBob oChris

sAnn {write} \varnothing \varnothing

sBob \varnothing {write} \varnothing

sChris \varnothing \varnothing {read}

m	oAnn	oBob	oChris
sAnn	{write}	`\varnothing`	`\varnothing`
sBob	`\varnothing`	{write}	`\varnothing`
sChris	`\varnothing`	`\varnothing`	{write}

m	oAnn	oBob	oChris
sAnn	{write}	`\varnothing`	`\varnothing`
sBob	`\varnothing`	{write}	`\varnothing`
sChris	`\varnothing`	`\varnothing`	{write, read}

m	oAnn	oBob	oChris
sAnn	{write}	`\varnothing`	`\varnothing`
sBob	`\varnothing`	{write}	`\varnothing`
sChris	`\varnothing`	`\varnothing`	{read}

Summary

Model Behavior
- The model’sinputis a sequence of actions from OP together with their respective arguments.
- The automaton changes its state according to the STS and the semantics of HRU primitives (here: enter and delete).
- In the initial state, each student may (repeatedly) submit her respective solution.
Tricks in this Example
- The sample solution is not represented by a separate object \rightarrow no separate column in the ACM.
- Instead, we smuggled thereadright for it into the cell of each student’s solution ...
Where Do We Stand?
- We can now model a security policy for particular IBAC scenarios
- We can formally express them through an automaton-based framework.
What’s Next? Why all this?
- Correct specification and implementation of the modeled policy
- Analysis of security properties \rightarrow Next ...

HRU Model Analysis

Reminder: "For a given security model, is it possible that a subjecteverobtains a specific permission with respect to a specific object?"
Analysis of Right Proliferation \rightarrow The HRU safety problem.

InputSequences

"What is the effect of an input in a given state?" \rightarrow asingle state transitionas defined by \delta
"What is the effect of an input sequence in a given state?" \rightarrow a composition ofsequential state transitionsas defined by \delta*

Transitive State Transition Function$\delta$^*

Let \sigma\sigma\in\sum^* be a sequence of inputs consisting of a single input \sigma\in\sum\cup\{\epsilon\} followed by a sequence \sigma\in\sum^^*, where \epsilon denotes an empty input sequence. Then, \delta^*:Q\times\sum^^*\rightarrow Q is defined by

\delta^*(q,\sigma\sigma^^*)=\delta^^*(\delta(q,\sigma),\sigma^^*)

\delta^^*(q,\epsilon)=q.

HRU Safety A state q of an HRU model is called HRU safe with respect to a right r\in R iff, beginning with q, there is no sequence of commands that enters r in an ACM cell where it did not exist in q. According to Tripunitara and Li [2013], this property (Due to more technical details, it’s called simple-safety there.) is defined as:

HRU Safety

For a state q=\{S_q,O_q,m_q\}\in Q and a right r\in R of an HRU model \langleQ,\sum,\delta,q_0,R\rangle, the predicate safe(q,r) holds iff \forall q′= S_{q′},O_{q′},m_{q′} \in \{\delta^^*(q,\sigma^^*)|\sigma^^*\in\sum^^*\},\forall s\in S_{q′},\forall o\in O_{q′}: r\in m_{q′}(s,o)\Rightarrow s\in S_q \wedge o\in O_q \wedge r\in m_q(s,o).

We say that an HRU model is safe w.r.t. r iff safe(q_0 ,r).

HRU Safety

Examples

Assume all states in {\delta^^*(q,\sigma^^*)|\sigma^^*\in\sum^^*\} have been validated except for q′:
- State transfer 1
  
  m_q o_1 o_2 o_3
  
  s_1 \{r_1,r_3\} \{r_1,r_3\} \{r_2\}
  
  s_2 ${r_1} \{r_1\} \{r_2\}
  
  s_3 \varnothing \varnothing \{r_2\}
  
  \Rightarrow \delta^*(q,\sigma^*)
  
  m_{q'} o_1 o_2 o_3
  
  s_1 \{r_1,r_3\} \{r_1\} \{r_2\}
  
  s_2 ${r_1,r_2} \{r_1\} \{r_2\}
  
  s_3 \varnothing \varnothing \varnothing
  - r_3\not\in m_{q′}(s_1,o_2)\wedge r_3\in m_q(s_1,o_1)\Rightarrow safe(q,r_3)
  - r_2\in m_{q′}(s_2,o_1)\wedge r_2 \not\in m_q(s_2,o_1)\Rightarrow\lnot safe(q,r_2)
- State transfer 2
  
  m_q o_1 o_2 o_3
  
  s_1 \{r_1,r_3\} \{r_1,r_3\} \{r_2\}
  
  s_2 ${r_1} \{r_1\} \{r_2\}
  
  s_3 \varnothing \varnothing \{r_2\}
  
  \Rightarrow \delta^*(q,\sigma^*)
  
  m_{q'} o_1 o_2 o_3 o_4
  
  s_1 \{r_1,r_3\} \{r_1,r_3\} \{r_2\} \varnothing
  
  s_2 ${r_1} \{r_1\} \{r_2\} \{r_2\}
  
  s_3 \varnothing \varnothing \{r_2\} \varnothing
  - \forall s\in S_{q′}:r_3\not\in m_{q′}(s,o_4)\wedge r_3\in m_q(s_1,o_1)\wedge r_3\in m_q(s_1,o_2)\Rightarrow safe(q,r_3)
  - r_2\in m_{q′}(s_2,o_4)\wedge o_4\not\in O_q\Rightarrow\lnot safe(q,r_2)

`m_q`	`o_1`	`o_2`	`o_3`
`s_1`	`\{r_1,r_3\}`	`\{r_1,r_3\}`	`\{r_2\}`
`s_2`	${r_1}	`\{r_1\}`	`\{r_2\}`
`s_3`	`\varnothing`	`\varnothing`	`\{r_2\}`

`m_{q'}`	`o_1`	`o_2`	`o_3`
`s_1`	`\{r_1,r_3\}`	`\{r_1\}`	`\{r_2\}`
`s_2`	${r_1,r_2}	`\{r_1\}`	`\{r_2\}`
`s_3`	`\varnothing`	`\varnothing`	`\varnothing`

`m_q`	`o_1`	`o_2`	`o_3`
`s_1`	`\{r_1,r_3\}`	`\{r_1,r_3\}`	`\{r_2\}`
`s_2`	${r_1}	`\{r_1\}`	`\{r_2\}`
`s_3`	`\varnothing`	`\varnothing`	`\{r_2\}`

`m_{q'}`	`o_1`	`o_2`	`o_3`	`o_4`
`s_1`	`\{r_1,r_3\}`	`\{r_1,r_3\}`	`\{r_2\}`	`\varnothing`
`s_2`	${r_1}	`\{r_1\}`	`\{r_2\}`	`\{r_2\}`
`s_3`	`\varnothing`	`\varnothing`	`\{r_2\}`	`\varnothing`

Let’s dissect the previous definitions: from a practical perspective, showing that an HRU model is safe w.r.t. r means to

Search for any possible (reachable) successor state q′ of q_0 ("${\delta(q_0,\sigma)|\sigma\in\sum}$")
Visit all cells in m_{q′} ("$\forall s\in S_{q′},\forall o\in O_{q′}:...$")
If r is found in one of these cells ("$r\in m_{q′}(s,o)$"), check if
- m_q is defined for this very cell ("$s\in S_q\wedge o\in O_q$"),
- r was already contained in this very cell in m_q ("$r\in m_q(s,o)$").
Recursively proceed with 2. for any possible successor state q′′ of q′ ("${\delta^^(q_0,\sigma^^)|\sigma^^\in\sum^^}$")

Safety Decidability

Theorem 1 [Harrison et al., 1976]

Ingeneral, HRU safety is not decidable.

Theorem 2 (also Harrison et al. [1976])

For mono-operational models, HRU safety is decidable.

"So ... what is amono-operational HRU model?" \rightarrow exactly one primitive for each operation in the STS:

command op(x_1 , ...,x_k) ::= if r_1 \in m(x_s1 ,x_o1 ) \wedge
    ... \wedge
    r_m \in m(x_sm,x_om)
  then
    p_1;
  fi

Theorem 1: See Harrison et al. [1976], reduction to the Halteproblem.
Theorem 2: We’ll have a closer look at this one ...
- Insights into the operational principles modeled by HRU models
- Demonstrates a method to prove safety property for a particular, given model
- \rightarrow "Proofs teach us how to build things so nothing more needs to be proven." (W. E. Kühnhauser)

Proof of Theorem

Proof Sketch
1. Find an upper bound for the length of all input sequences with different effects on the protection state w.r.t. safety If such can be found: \exists a finite number of input sequences with different effects
2. All these inputs can be tested whether they violate safety. This test terminates because:
- each input sequence is finite
- there is only a finite number of relevant sequences
- \rightarrow safety is decidable

Given a mono-operational HRU model. Let \sigma_1...\sigma_n be any sequence of inputs in \sum^* that violates safe(q,r), and let p_1...p_n be the corresponding sequence of primitives (same length, since mono-operational).

Proposition: For each such sequence, there is a corresponding finite sequence that

Still violates safe(q,r)
Consists only of enter and two initial create primitives

In other words: For any input sequence,$\exists$ a finite sequence with the same effect.

Proof:

We construct these finite sequences ...$\rightarrow$
Transform \sigma_1...\sigma_n into shorter sequences with the same effect:
1. Remove all input operations that contain delete or destroy primitives. The sequence still violates safe(q,r), because conditions of successive commands must still be satisfied (no absence, only presence of rights is checked).
2. Prepend the sequence with an initial create subject s_{init} operation. This won’t change its netto effect, because the new subject isn’t used anywhere.
3. Prune the last create subject s operation and substitute each following reference to s with s_{init}. Repeat until allcreate subjectoperations are removed, except from the initialcreate subject sinit.
4. Same as steps 2 and 3 for objects.
5. Remove all redundant enter operations (remember: each matrix cell is a set \rightarrow unique elements).

Example:

init	1.	2.	3.	4.	5.
...	...	create subject `s_{init}`;	create subject `s_{init}`;	create subject `s_{init}`;	create subject `s_{init}`;
...	...	...	...	create object `o_{init}`	create object `o_{init}`
create subject x2;	create subject x2;	create subject x2;	-	-	-
create object x5;	create object x5;	create object x5;	create object x5;	-	-
enter r1 into m(x2,x5);	enter r1 into m(x2,x5);	enter r1 into m(x2,x5);	enter r1 into `m(s_{init},x5)`;	enter r1 into `m(s_{init},o_{init})`;	enter r1 into `m(s_{init},o_{init})`;
enter r2 into m(x2,x5);	enter r2 into m(x2,x5);	enter r2 into m(x2,x5);	enter r2 into `m(s_{init},x5)`;	enter r2 into `m(s_{init},o_{init})`;	enter r2 into `m(s_{init},o_{init})`;
create subject x7;	create subject x7;	create subject x7;	-	-	-
delete r1 from m(x2,x5);	-	-	-	-	-
destroy subject x2;	-	-	-	-	-
enter r1 into m(x7,x5);	enter r1 into m(x7,x5);	enter r1 into m(x7,x5);	enter r1 into `m(s_{init},x5)`;	enter r1 into `m(s_{init},o_{init})`;	-
...	...	...	...	...	...

Observations

after step 3:
- Except for s_{init}, the sequence creates no more subjects
- All rights of the formerly created subjects are accumulated in s_{init}\rightarrow for the evaluation of safe(q,r), nothing has changed:
  - generally: \forall s\in S_{q′},\forall o\in O_{q′}:r\in m_{q′}(s,o)\Rightarrow s\in S_q\wedge o\in O_q\wedge r\in m_q(s,o)
  - in this case: \forall s\in S_{q′},\forall o\in O_{q′}:r\in m_{q′}(s,o)\Rightarrow s\not=s_{init}\wedge o\in O_q\wedge r\in m_q(s,o)
- The sequence is generally shorter (never longer) than before
Final Observations
- Except for s_{init} and o_{init}, the sequence creates no subjects or objects
- All entered rights are accumulated in m_{q′}(s_{init},o_{init}):
  - generally: \forall s\in S_{q′},\forall o\in O_{q′}:r\in m_{q′}(s,o)\Rightarrow s\in S_q\wedge o\in O_q\wedge r\in m_q(s,o)
  - here: \forall s\in S_{q′},\forall o\in O_{q′}:r\in m_{q′}(s,o)\Rightarrow s\not=s_{init}\wedge o\not=o_{init}\wedge r\in m_q(s,o)
- This sequence still violates safe(q,r), but its length is restricted to (|S_q| + 1)(|O_q|+1)|R|+2 because
  - Each enter must enter a new right into a cell
  - The number of cells is restricted to (|S_q| + 1)(|O_q|+1)

Conclusions from these Theorems

Dilemma:
- General (unrestricted) HRU models
  - have strong expressiveness \rightarrow can model a broad range of AC policies
  - are hard to analyze: algorithms and tools for safety analysis
    - \rightarrow cannot certainly produce accurate results
    - \rightarrow are hard to design for approximative results
- Mono-operational HRU models
  - have weak expressiveness \rightarrow goes as far as uselessness: e. g. for modeling Unix creat(can only create files, sockets, IPC, ... that no user process can access!)
  - are efficient to analyze: algorithms and tools for safety analysis
  - \rightarrow are always guaranteed to terminate
  - \rightarrow are straight-forward to design

Consequences:

Model variants with restricted yet usable expressiveness have been proposed
Heuristic analysis methods try to provide educated guesses about safety of unrestricted HRU

(A) Restricted Model Variants

Static HRU Models

Static: no create primitives allowed
safe(q,r) decidable, but NP-complete problem
Applications: (static) real-time systems, closed embedded systems

Monotonous Mono-conditional HRU Models

Monotonous (MHRU): no delete or destroy primitives
Mono-conditional: at most one clause in conditions part (For monotonous bi-conditional models, safety is already undecidable ...)
safe(q,r) efficiently decidable
Applications: Archiving/logging systems (where nothing is ever deleted)

Finite Subject Set

\forall q\in Q,\exists n\in N: |S_q|\leq n
safe(q,r) decidable, but high computational complexity

Fixed STS

All STS commands are fixed, match particular application domain (e.g. OS access control [Lipton and Snyder, 1977]) \rightarrow no model reusability
For Lipton and Snyder [1977]: safe(q,r) decidable in linear time (!)

Strong Type System

Special model that generalizes HRU: Typed Access Matrix (TAM) [Sandhu, 1992]
safe(q,r) decidable in polynomial time for ternary, acyclic, monotonous variants
high, though not unrestricted expressiveness in practice

(B) Heuristic Analysis Methods

Motivation:

Restricted model variants: often too weak for real-world applications
General HRU models: safety property cannot be guaranteed \rightarrow Let’s try to get a piece from both cakes: Heuristically guided safety estimation [Amthor et al., 2013]

Idea:

State-space exploration by model simulation
Task of heuristic: generating input sequences ("educated guessing")

Outline: Two-phase-algorithm to analyze safe(q_0,r):

Static phase: Infer knowledge from the model that helps heuristic to make "good" decisions.
- \rightarrow Runtime: polynomial in model size (q_0 + STS)
Simulation phase: The automaton is implemented and, starting with q_0, fed with inputs \sigma=\langleop,x\rangle
- \rightarrow For each \sigma, the heuristic has to decide:
  - which operation op to use
  - which vector of arguments x to pass
  - which q_i to use from the states in Q known so far
- Termination: As soon as \sigma(q_i,\sigma) violates safe(q_0,r).

Goal: Iteratively build up the (possibly infinite!) Q for a model to falsify safety by example (finding a violating, but possible protection state).

Results:

Termination: Well ... we only have a semi-decidable problem here: It can be guaranteed that a model is unsafe if we terminate. We cannot ever prove the opposite, however! (\rightarrow safety undecidability)
Performance: A few results
- 2013:Model size 10 000 ≈2215 s
- 2018:Model size 10 000 ≈0,36 s
- 2018:Model size 10 000 000 ≈417 s

Achievements:

Find typical errors in security policies: Guide their designers, who might know there’s something wrong w. r. t. right proliferation, but not what and why!
Increase our understanding of unsafety origins: By building clever heuristics, we started to understand how we might design specialized HRU models (\rightarrow fixed STS, type system) that are safety-decidable yet practically (re-) usable [Amthor and Rabe, 2020].

Summary HRU Models

Goal

Analysis of right proliferation in AC models
Assessing the computational complexity of such analyses

Method

Combining ACMs and deterministic automata
Defining safe(q,r) based on this formalism

Conclusions

Potential right proliferation (privilege escalation): Generally undecidable problem
\rightarrow HRUmodel family, consisting of application-tailored, safety-decidable variants
\rightarrow Heuristic analysis methods for practical error-finding

The Typed-Access-Matrix Model (TAM)

Goal

AC model, similar expressiveness to HRU
\rightarrow can be directly mapped to implementations of an ACM: OS ACLs, DB permission assignment tables
Better suited for safety analyses: precisely statemodel properties for decidable safety

Idea [Sandhu, 1992]

Adopted from HRU: subjects, objects, ACM, automaton
New: leverage the principle of strong typing known from programming
\rightarrow safety decidability properties relate to type-based restrictions

How it Works:

Foundation of a TAM model is an HRU model \langleQ,\sum,\delta,q_0 ,R\rangle, where Q= 2^S\times 2^O\times M
However: S\subseteq O, i. e.:
- all subjects can also act as objects (=targets of an access)
- \rightarrow useful for modeling e. g. delegation ("s has the right to grant s′ her read-right")
- objects in O\backslash S: pure objects
Each o\in O has a type from a type set T assigned through a mapping type:O\rightarrow T
An HRU model is a special case of a TAM model:
- T=\{tSubject,tObject\}
- \forall s\in S:type(s)=tSubject; \forall o\in O\backslash S:type(o)=tObject

TAM Security Model

A TAM model is a deterministic automaton \langleQ,\sum,\delta,q_0 ,T,R\rangle where

Q= 2^S\times 2^O\times TYPE\times M is the state space where S and O are subjects set and objects set as in HRU, where S\subseteq O, TYPE=\{type|type:O\rightarrow T\} is a set of possible type functions, M is the set of possible ACMs as in HRU,

\sum=OP\times X is the (finite) input alphabet where OP is a set of operations as in HRU, X=O^k is a set of $k$-dimensional vectors of arguments (objects) of these operations,

\delta:Q\times\sum\rightarrow Q is the state transition function,

q_0\in Q is the initial state,

T is a static (finite) set of types,

R is a (finite) set of access rights.

State Transition Scheme (STS) \delta:Q\times\sum\rightarrow Q is defined by a set of specifications: where

q= (S_q,O_q,type_q,m_q)\in Q,op\in OP
r_1,...,r_m\in R
x_{s1},...,x_{sm}\in S_q,x_{o1},...,x_{om}\in Oq\backslash S_q, and t_1,...,t_k\in T where s_i and o_i, 1\leq i\leq m , are vector indices of the input arguments: 1\leq s_i,o_i\leq k
p_1,...,p_n are TAM primitives
Convenience Notation where
- q\in Q is implicit
- op,r_1 ,...,r_m,s_1 ,...,s_m,o_1 ,...,o_m as before
- t_1 ,...,t_k are argument types
- p_1 ,...,p_n are TAM-specific primitives
TAM-specific
- Implicit Add-on:Type Checking
- where t_i are the types of the arguments x_i, 1\leq i\leq k.

TAM-specific

Primitives:
- enter r into m(x_s,x_o)
- delete r from m(x_s,x_o)
- create subject x_s of type t_s
- create object x_o of type t_o
- destroy subject x_s
- destroy object x_o
Observation: S and O are dynamic (as in HRU), thus type:O\rightarrow T must be dynamic too (cf. definition of Q in TAM).

TAM Example: The ORCON Policy

Example Scenario: Originator Controlled Access Rights (ORCON Policy)
Goal: To illustrate usefulness/convenience of type system
- ORCON describes sub-problem of larger policies
- Information flow confinement required by ORCON is tricky to do in HRU ("This information may not flow beyond ...")
The Problem
- Creator/owner of a document shouldpermanently retain controlover its accesses
- Neither direct nor indirect (by copying) right proliferation
- Application scenarios: Digital rights management, confidential sharing (online social networks!)
Solution with TAM
- Idea: A confined subject type that can never execute any operation other than reading
- Model Initialization:
  - Subjects: S_0=\{ann,bob,chris\}
  - Objects: O_0 =S_0\cup\{projectX\}
  - Operations: \rightarrow next ...
  - Rights: R=\{read,write,cread,own,parent\}
  - Types: T=\{s,cs,co\} (regular subject,confined subject/object)
  - type_0:
    - type_0(ann)=s
    - type_0(bob)=s
    - type_0(projectX)=co

Model Behavior (Example)

ann creates ORCON object projectX (STS command createOrconObject)
ann grants cread ("confined read") right for projectX to bob (STS command grantCRead)

bob uses cread to create confined subject chris with permission to read projectX (STS command useCRead)

m	ann:s	bob:s	projectX:co	chris:cs
ann:s	`\varnothing`	`\varnothing`	`\{own, read, write\}`	`\varnothing`
bob:s	`\varnothing`	`\varnothing`	`\{cread\}`	`\{parent\}`
chris:cs	`\varnothing`	`\varnothing`	`\{read\}`	`\varnothing`

Model Behavior (STS)

The State Transition Scheme

createOrconObject

command createOrconObject(s_1:s, o_1:co) ::=
  if true
  then
    create object o_1 of type co;
    enter own into m(s_1 ,o_1);
    enter read into m(s_1 ,o_1);
    enter write into m(s_1 ,o_1);
  fi

grantCRead

command grantCRead(s 1 :s,s 2 :s,o 1 :co) ::=
  if own ∈ m(s_1 ,o_1)
  then
    enter cread into m(s_2 ,o_1);
  fi

useCRead

command useCRead(s_1:s, o_1:co, s_2:cs) ::=
  if cread ∈ m(s_1 ,o_1)
  then
    create subject s_2 of type cs;
    enter parent into m(s_1 ,s_2);
    enter readinto m(s_2 ,o_1);
  fi

Enable ann to revoke cread from bob:

command revokeCRead(s_1:s, s_2:s, o_1:co) ::=
  if own ∈ m(s_1, o_1)
  then
    delete cread from m(s_2, o_1);
  fi

Enable ann to destroy conf. object projectX:

command destroyOrconObject(s_1:s, o_1:co) ::=
  if own ∈ m(s_1 ,o_1)
  then
    destroy object o_1;
  fi

Enable ann to destroy conf. subject chris:

command revokeRead(s_1:s, s_2:cs, o_1:co) ::= 
  if own ∈ m(s_1 ,o_1) ∧ read ∈ m(s_2 ,o_1)
  then
    destroy subject s_2;
  fi

Enable bob to destroy conf. subject chris:

command finishOrconRead(s_1:s, s_2:cs) ::= 
  if parent ∈ m(s_1, s_2)
  then
    destroy subject s_2;
  fi

Commands 1.-3.:
- Authorize the steps in the example above
- Are monotonic
Commands 4.-7.:
- Will control right revocation \rightarrow essence of originator control
- Are not monotonic (consequences ...)

Summary
- Contributions of ORCON Example
  - Owner ("originator") retains full control over
    - Use of her confined objects by third parties \rightarrow transitive right revocation
    - Subjects using (or misusing) these objects \rightarrow destruction of these subjects
  - Subjects using such objects are confined: cannot forward read information

TAM Safety Decidability

Why all this?

General TAM models (cf. previous definition) \rightarrow safety not decidable (no surprise, since generalization of HRU)
MTAM:monotonous TAM models; STS without delete or destroy primitives \rightarrow safety decidable if mono-conditional only
AMTAM:acyclic MTAM models \rightarrow safety decidable, but (most likely) not efficiently: NP-hardproblem
TAMTAM: ternaryAMTAM models; each STS command requires max. 3 arguments \rightarrow provably same computational power and thus expressive power as AMTAM; safety decidable in polynomial time

Acyclic TAM Models

Auxiliary analysis tools for TAM models:

Parent- and Child-Types

For any operation op with arguments \langlex_1,t_1\rangle,\langlex_2,t_2\rangle,...,\langlex_k,t_k\rangle in an STS of a TAM model, it holds that t_i, 1\leq i\leq k

is a child type in op if one of its primitives creates a subject or object x_i of type t_i,

is a parent type in op if none of its primitives creates a subject or object x_i of type t_i.

Type Creation Graph

The type creation graph TCG=\langleT,E=T\times T\rangle for the STS of a TAM model is a directed graph with vertex set T and an edge\langleu,v\rangle\in E iff \exists op\in OP:u is a parent type in op\wedge v is a child type in op.

Example STS:

command foo(s_1:u, o_1:w, o_2:v) ::=
  if r_1 ∈ m(s_1 ,o_1)
  then
    create object o_2 of type v;
  fi

command bar(s_1:u, s_2:u, s_3:v, o_1:w) ::=
  if r_2 ∈ m(s_1 ,o_1)
  then
    create subject s_2 of type u;
    create subject s_3 of type v;
  fi

Note:In bar,u is both a parent type (because of s_1) and a child type (because of s_2) \rightarrow hence the loop edge.

Safety Decidability

We call a TAM model acyclic, iff its TCG is acyclic.

Theorem [Sandhu, 1992, Theorem 5]

Safety of a ternary, acyclic, monotonous TAM model (TAMTAM) is decidable in polynomial time in the size of m_0.

Crucial property acyclic, intuitively:
- Evolution of the system (protection state transitions) checks both rights in the ACMas well as argument types
- TCG is acyclic \Rightarrow\exists a finite sequence of possible state transitions after which no input tuple with argument types, that were not already considered before, can be found
- One may prove that an algorithm, which tries to expandall possible different follow-up states from q_0, may terminate after this finite sequence
- Proof details: SeeSandhu [1992].

Expressive Power of TAMTAM

MTAM: obviously same expressive power as monotonic HRU (MHRU) \rightarrow cannot model:
- transfer of rights: "take r from ... and in turn grant r to ..."
- countdown rights: "r can only be used n times"
ORCON example (and many others): allow to ignore non-monotonic command s from STS, e.g. 4.-7., since they
- only remove rights
- are reversible (e. g.: undo 4. by 2.; compensate 7. by 3. where the new subject takes roles of the destroyed one)
AMTAM: most MTAM STS may be re-written as acyclic(cf. ORCON example)
TAMTAM: expressive power equivalent to AMTAM

IBAC Model Comparison

So far: family of IBAC models to describe different ranges of security policies they are able to express(depicted as an Euler diagram):

IBAC Summary

We May Now
- Model identity-based AC policies (IBAC)
- Analyze them w. r. t. basic security properties (right proliferation)
- \rightarrow Minimize specification errors
- \rightarrow Minimize implementation errors
Approach
- Unambiguous policy representation through formal notation
- Prediction and/or verification of mission-critical properties
- Derivation of implementation concepts
Model Range
- Static models:
  - Access control function (ACF): f:S\times O\times OP\rightarrow \{true,false\}
  - Access control matrix (ACM): m:S\times O\rightarrow 2^{OP}
  - \rightarrow Static analysis: Which rights are assigned to whom, which (indirect) information flows are possible
  - \rightarrow Implementation: Access control lists (ACLs), e.g. in OS, (DB)IS
- Dynamic models:
  - ACM plus deterministic automaton \rightarrow Analysis of dynamic behavior: HRU safety
    - generally undecidable
    - decidable under specific restrictions: monotonous mono-conditional, static, typed, etc.
    - identifying and explaining safety-violations, in case such (are assumed to) exists: heuristic analysis algorithms
Limitations
- IBAC models are fundamental: KISS
- IBAC models provide basic expressiveness only:
  - Comparable to "assembler programs for writing AC policies"
  - Imagine writing a sophisticated end-user application in assembler:
    - reserve and keep track of memory layout and addresses ≈ create and maintain individual rights for thousands of subjects, billions of objects
    - display comfortable GUI by writing to the video card framebuffer ≈ specify sophisticated workflows through an HRU STS
- For more application-oriented policy semantics:
  - Large information systems: many users, many databases, files, ... \rightarrow Scalability problem
  - Access decisions not just based on subjects, objects, and operations \rightarrow Abstraction problem

\rightarrow "New" paradigm (early-mid 90s): Role-based Access Control

Roles-based Access Control Models (RBAC)

Self-Study Task

Have a look at the syscall API of Linux as a typical file server operating system. Roughly count the number of operations and estimate their average number of arguments based on a few samples. Then try to estimate the average number of files that each user keeps in her home directory. A good sample is your own user directory, which you can count (including subdirectories) as follows: find ~ -type f | wc -l

If 200 employees of a medium-sized company have user accounts:

How many ACLs must be saved to encode the IBAC policy of this server as a classical ACM?

If each ACL take 12 bits, how big is the resulting storage overhead in total?

If you had to heuristically analyze safety of this policy: how many different inputs would you have to simulate in the worst case just for the first state transition?

Problems of IBAC Models:

Scalability w.r.t. the number of controlled entities
Level of abstraction: System-oriented policy semantics (processes, files, databases, ...) instead of problem-oriented (management levels, user accounts, quota, ...)

Goals of RBAC:

Solving these problems results in smaller modeling effort results in smaller chance of human errors made in the process:
- Improved scalability and manageability
- Improved, application-oriented semantics: roles≈functions in organizations

RBAC Application Domains

Public health care systems
- Roles: Patient, physician, therapist, pharmacist, insurer, legislator, ...
Financial services
- Roles: Client, consultant, analyst, product manager, ...
Operating systems
- Roles: System admin, webserver admin, database admin, key account user, user, ...

RBAC Idea

Models include smart abstraction: roles
Access control rules are specified based on roles instead of identities:
- "All ward physiciansare allowed to read EPRs."
- "Allnursesare allowed to log body temperature."
Compared to IBAC
- IBAC Semantics:
  - Subjects, objects, and rights for executing operations
  - Access rules are based onidentity of individualsubjects and objects
- RBAC Semantics:
  - Users, roles, and rights for executing operations
  - Access rules are based onrolesof users \rightarrow on assignments:

RBAC Security Model Definition

Basic RBAC model: "$RBAC_0$" [Sandhu, 1994]:

An RBAC 0 model is a tuple \langleU,R,P,S,UA,PA,user,roles\rangle where

U is a set of user identifiers,

R is a set of role identifiers,

P is a set of permission identifiers,

S is a set of session identifiers,

UA\subseteq U\times R is a many-to-many user-role-relation,

PA\subseteq P\times R is a many-to-many permission-role-relation,

user:S\rightarrow U is a total function mapping sessions to users,

roles:S\rightarrow 2^R is a total function mapping sessions to sets of roles such that \forall s\in S:r\in roles(s)\Rightarrow \langleuser(s),r\rangle\in UA.

Interpretation

Users U model people: actual humans that operate the AC system
Roles R model functions (accumulations of tasks), that originate from the workflows and areas of responsibility in organizations
Permissions P model rights for any particular access to a particular document (e. g. read project documentation, transfer money, write into EPR, ...)
The user-role-relation UA\subseteq U\times R defines which roles are available to users at any given time \rightarrow must be assumed during runtime first, before they are usable!
The permission-role-relation PA\subseteq P\times R defines which permissions are associate with roles
UA and PA describe static policy rules: Roles available to a user are not considered to possibly change, same with permissions associated with a role. Examples:
- "Bob may assume the role of a developer; Ann may assume the role of a developer or a project manager; ..."
- "A developer may read and write the project documentation; a project manager may create branches of a source code repository; ..."
Sessions S describe dynamic assignments of roles \rightarrow a session s\in S models when a user is logged in(where she may use some role(s) available to her as per UA):
- The session-user-mapping user: S\rightarrow U associates a session with its ("owning") user
- The session-roles-mapping roles: S\rightarrow 2^R associates a session with the set of roles currently assumed by that user (active roles)

Remark: Note the difference between users in RBAC and subjects in IBAC: the latter usually represent a technical abstraction, such as an OS process, while RBAC users always model an organizational abstraction, such as an employee, a patient, etc.!

RBAC Access Control Function

Authorization in practice: access rules have to be defined for operations on objects (cf. IBAC)
IBAC approach: access control function f:S\times O\times OP\rightarrow \{true,false\}
RBAC approach: implicitly defined through P\rightarrow made explicit: P\subseteq O\times OP is a set of permission tuples \langleo,op\rangle where
- o\in O is an object from a set of object identifiers,
- op\in OP is an operation from a set of operation identifiers.
We may now define the ACF for RBAC_0:

RBAC_0 ACF

f_{RBAC_0}:U \times O\times OP\rightarrow\{true,false\} where f_{RBAC_0} (u,o,op)= \begin{cases} true, \quad \exists r\in R,s\in S:u=user(s)\wedge r\in roles(s)\wedge \langle\langleo,op\rangle,r\rangle \in PA \\ false, \quad\text{ otherwise\end{cases}.

RBAC96 Model Family

Sandhu et al. [1996]

In practice, organizations have more requirements that need to be expressed in their security policy:

Roles are often hierarchical: "Any project manager is also a developer, any medical director is also a doctor, ..." \rightarrow RBAC_1 = RBAC_0 + hierarchies
Role association and activation are often constrained: "No purchasing manager may be head of internal auditing, no product manager may be logged in as a project manager for more than one project at a time, ..." \rightarrow RBAC_2 = RBAC_0 + constraints
Both may be needed: \rightarrow RBAC_3 = consolidation: RBAC_0 + RBAC_1 + RBAC_2

RBAC 1 : Role Hierarchies

Observation: Roles in organizations often overlap:
- Users in different roles havecommon permissions: "Any project manager must have the same permissions as any developer in the same project."
- Approach 1: disjoint permissions for roles proManager and proDev \rightarrow any proManager user must always have proDev assigned and activated for any of her workflows \rightarrow role assignment redundancy
- Approach 2: overlapping permissions: \forall p\in P:\langlep,proDev\rangle \in PA\Rightarrow \langlep,proManager\rangle \in PA\rightarrow any permission for project developers must be assigned to two different roles \rightarrow role definition redundancy
- Two types of redundancy \rightarrow undermines scalability goal of RBAC!
Solution
- Role hierarchy: Eliminates role definition redundancy through permissions inheritance
Modeling Role Hierarchies
- Lattice here: \langleR,\leq\rangle
- Hierarchy expressed through dominance relation: r_1\leq r_2 \Leftrightarrow r_2 inherits any permissions from r_1
- Interpretation
  - Reflexivity: any role consists of ("inherits") its own permissions \forall r\in R:r\leq r
  - Antisymmetry: no two different roles may mutually inherit their respective permissions \forall r_1 ,r_2\in R:r_1\leq r_2\wedge r_2\leq r_1\Rightarrow r_1=r_2
  - Transitivity: permissions may be inherited indirectly \forall r_1,r_2,r_3\in R:r_1\leq r_2 \wedge r_2\leq r_3\Rightarrow r_1\leq r_3

RBAC_1 Security Model

An RBAC_1 model is a tuple \langleU,R,P,S,UA,PA,user,roles,RH\rangle where

U,R,P,S,UA,PA and user are defined as for RBAC_0,

RH\subseteq R\times R is a partial order that represents a role hierarchy where \langler,r′\rangle\in RH\Leftrightarrow r\leq r′ such that \langleR,\leq\rangle is a lattice,

roles is defined as for RBAC_0, while additionally holds: \forall r,r′\in R,\exists s\in S:r\leq r′\wedge r′\in roles(s)\Rightarrow r\in roles(s).

In prose: When activating any role that inherits permissions from another role, this other role isautomatically(by definition) active as well.

\rightarrow no role assignment redundancy in defining the STS
\rightarrow no role definition redundancy in defining PA

RBAC 2 : Constraints

Observation: Assuming and activating roles in organizations is often more restricted:
- Certain roles may not beactive at the same time(same session)for any user: "A payment initiator may not be a payment authorizer at the same time (in the same session)."
- Certain roles may not be together assigned to any user: "A purchasing manager never be the same person as the head of internal auditing."
- \rightarrow separation of duty (SoD)
- While SoD constraints are a more fine-grained type of security requirements to avoid mission-critical risks, there are other types represented by RBAC constraints.
Constraint Types
- Separation of duty: mutually exclusive roles
- Quantitative constraints: maximum number of roles per user
- Temporal constraints: time/date/week/... of role activation (advanced RBAC models, e.g. Bertino et al. [2001])
- Factual constraints: assigning or activating roles for specific permissions causally depends on any roles for a certain, other permissions (e.g. only allow user u to activate auditingDelegator role if audit payments permission is usable by u)
Modeling Constraints:(idea only)
- RBAC_2 : \langleU,R,P,S,UA,PA,user,roles,RE\rangle
- RBAC_3 : \langleU,R,P,S,UA,PA,user,roles,RH,RE\rangle
- where RE is aset of logical expressions over the other model components (such as UA,PA,user,roles).

RBAC Summary

Scalability
Application-oriented model abstractions
Standardization (RBAC96) \rightarrow tool-support for:
- role engineering (identifying and modeling roles)
- model engineering (specifying and validating a model configuration)
- static model checking (verifying consistency and plausibility of a model configuration)
Still weak OS-support
- \rightarrow application-level integrations (e. g. hospital IS, DBIS, ERP systems)
- \rightarrow middleware integrations (e. g. XACML, NGAC[Ferraiolo et al., 2016])
Limited dynamic analyses w.r.t. automaton-based models
- cf. HRU:safety properties?
- solution approach: automaton-based RBAC96 model
  - \rightarrow DRBAC 0 ... 3 [Schlegel and Amthor, 2020]

Attribute-based Access Control Models

Goals of ABAC:

Providing a more versatile solution than RBAC for these problems, especially for open and distributed systems.
- Scalability and manageability
- Application-oriented model abstractions
- Model semantics meet functional requirements of open systems:
  - user IDs, INode IDs, ... only available locally, scaling bad
  - roles that gather permissions model functions limited to specific organizational structure; only assignable to users
- \rightarrow Consider application-specific context of an access: attributes of subjects and objects(e. g. age, location, trust level, ...)

Idea: Generalizing the principle of indirection already known from RBAC

IBAC: no indirection between subjects and objects
RBAC: indirection via roles assigned to subjects
ABAC: indirection via arbitrary attributes assigned to subjects or objects
Attributes model application-specific properties of the system entities involved in any access, e. g.:
- Age, location, trustworthiness of a application/user/device/...
- Size, creation time, premium-access classification of web resource/multimedia content/document/...
- Risk quantification involved with these subjects and objects (e. g. access from an IP address/proxy domain reportedly belonging to a TOR network)

ABAC Access Control Function

f_{IBAC}:S\times O\times OP\rightarrow\{true,false\}
f_{RBAC}:U\times O\times OP\rightarrow\{true,false\}
f_{ABAC}:S\times O\times OP\rightarrow\{true,false\}
\rightarrow Evaluates attribute values for \langles,o,op\rangle, e. g.: f_{ABAC}(user,game,download)=game.pegi \leq user.age

ABAC Security Model

Note: There is no such thing (yet) like a standard ABAC model (such as RBAC96).
Instead: Many highly specialized, application-specific models.
Here: minimal common formalism, based on Servos and Osborn [2017]

ABAC Security Model

An ABAC security model is a tuple \langleS,O,AS,AO,attS,attO,OP,AAR\rangle where

S is a set of subject identifiers and O is a set of object identifiers,

A_S=V_S^1 \times...\times V_S^n is a set of subject attributes, where each attribute is an n-tuple of values from arbitrary domains V_S^i, 1\leq i \leq n,

A_O=V_O^1\times...\times V_O^m is a corresponding set of object attributes, based on values from arbitrary domains V_O^j, 1\leq j \leq m,

att_S:S\rightarrow A_S is the subject attribute assignment function,

att_O:O\rightarrow A_O is the object attribute assignment function,

OP is a set of operation identifiers,

AAR\subseteq \Phi\times OP is the authorization relation.

Interpretation

Active and passive entities are modeled by S and O, respectively
Attributes in AS,AO are index-referenced tuples of values, which are specific to some property of subjects V_S^i (e.g. age) or of objects V_O^j (e. g. PEGI rating)
Attributes are assigned to subjects and objects via att_S,att_O
Access control rules w.r.t. the execution of operations in OP are modeled by the AAR relation \rightarrow determines ACF!
AAR is based on aset of first-order logic predicates \Phi: \Phi=\{\phi_1 (x_{s1},x_{o1}),\phi_2 (x_{s2},x_{o2}),...\}. Each \phi_i\in\Phi is a binary predicate (a logical statement with two arguments), where x_{si} is a subject variable and x_{oi} is an object variable.

ABAC Access Control Function

With conditions from \Phi for executing operations in OP,AAR determines the ACF of the model:

ABAC ACF

f_{ABAC}:S\times O\times OP\rightarrow\{true,false\} where f_{ABAC}(s,o,op)= \begin{cases} true, \quad\exists \langle\phi,op\rangle\in AAR:\phi(s,o)=true\\ false, \quad\text{ otherwise }. We call \phi an authorization predicate for op.

Example 1: Online Game Store

Policy goal: Enforce PEGI age restrictions for video game access
S: set of client IDs
O: set of video game titles
A_S=\mathbb{N}(where\ n=1): one subject attribute (age)
A_O=\{0,3,7,12,14,18\}(where\ m=1): one object attribute (PEGI rating)
att_S:S\rightarrow A_S: assigns age attribute to clients
att_O:O\rightarrow A_O: assigns PEGI rating attribute to games
OP=\{download\}: sole operation
One simpleauthorization rule: AAR=\{\langleatt_O(o) \leq att_S(s),download\rangle\}

Example 2: Document Management System

Policy goal: Enforce document confidentiality
S: set of user IDs
O: set of document IDs
A_S=\mathbb{N}(where\ n=1): subject attribute (trustworthiness value)
A_O=\mathbb{N}(where\ m=1): object attribute (confidentiality level)
att_S:S\rightarrow A_S: assigns trustworthiness value to user (e. g. based on management level)
att_O:O\rightarrow A_O: assigns confidentiality level to documents
OP=\{read,write,append,...\}: operations
Authorization rules: AAR=\{\langleatt_O(o)\leq att_S(s),read\rangle,\langleatt_S(s) \leq att_O(o),write\rangle,...\}

ABAC Summary

Scalability
Application-oriented model abstractions
Universality: ABAC can conveniently express
- IBAC (attributes: IDs)
- RBAC (attributes: roles)
- MLS (attributes: sensitivity levels \rightarrow next topic)
Still weak OS-support \rightarrow application-level integrations (increasingly replacing RBAC)
Attribute semantics highly diverse, not normalizable \rightarrow no common "standard ABAC" to expect (all too soon ...)
Limited dynamic analyses w.r.t. automaton-based models
- cf. HRU:safety properties?
- solution approach: automaton-based ABAC model ...

Information Flow Models

Abstraction Level of AC Models: rules about subjects accessing objects

Adequate for

Workflow systems
Document/information management systems
... that’s it.

Goal of Information Flow (IF) Models: Problem-oriented definition of policy rules for scenarios based on information flows(rather than access rights)

Lattices (refreshment)

Terms:
- inf_C: "systemlow"
- sup_C: "systemhigh"
\rightarrow notably, a graph described by a lattice
is connected
has a source: deg^-(inf_C)= 0
has a sink: deg^+(sup_C)= 0

Self-Study Task

I work on six days in a week:W=\{Mo,Tu,We,Th,Fr,Sa\}. On each of these days, I can decide to procrastinate work from daywtow′: the same or a day later that week (w\rightarrow w′). For a lattice \langleW,\rightarrow\rangle:

Draw the lattice as a graph.

Determine inf_W and sup_W. Let’s assume that Saturday is exclusively reserved for work I was unable to do on Monday. Is \langleW,\rightarrow\rangle still a lattice now? Why (not)?

Implementation of Information Flow Models

Background: Information flows and read/write operations are isomorphic
- s has read permission w.r.t. o \Leftrightarrow information may flow from o to s
- s has write permission w.r.t. o \Leftrightarrow information may flow from s to o
\rightarrow Implementation by standard AC mechanisms!

Analysis of Information Flow Models

IF Transitivity \rightarrow analysis goal: covert information flows
- Question: "Is there a possible, sequential usage of read- and write-permissions that ultimately leads to an unintended information flow?"
IF Antisymmetry \rightarrow analysis goal: redundancy
- Question: "Which subjects/object share the same possible information flows and are therefore redundant?"

The Denning Model

On of the first information flow models [Denning, 1976]:

Denning Security Model

A Denning information flow model is a tuple \langleS,O,L,cl,\bigoplus\rangle where

S is a set of subjects,

O is a set of objects,

L=\langleC,\leq\rangle is a lattice where

C is a set of classes,

\leq is a dominance relation wherec \leq d \Leftrightarrow information may flow from c to d,

cl:S\cup O\rightarrow C is a classification function, and

\bigoplus:C\times C\rightarrow C is a reclassification function.

Interpretation

Subject set S models active entities, which information flows originate from
Object set O models passive entities, which may receive information flows (e.g. documents)
Classes set C used to label entities with identical information flow properties, e.g. C=\{Physician,Patient\}
Classification function cl assigns a class to each entity, e.g. cl(cox)=Physician
Reclassification function \bigoplus determines which class an entity is assigned after receiving certain a information flow; e.g. for Physician to Patient: \bigoplus (Physician,Patient)=sup_{\{Physician,Patient\}}

Example \langleS,O,L,cl,\bigoplus\rangle mit L=\langleC,\leq\rangle:

S=O=\{cox,kelso,carla,...\}
C=\{Physician, Anamnesis, Pharmacy, Medication,...\}
dominance relation \leq:
- rule "information may flow from any ward physician to an anamnesis record" \Leftrightarrow Physician \leq Anamnesis
- rule "information may flow from a medication record to the pharmacy" \Leftrightarrow Medication \leq Pharmacy
classification cl:
- cox=Physician
- carla=Medication

We can now ...

precisely define all information flows valid for a given policy
define analysis goals for an IF model w.r.t.
- Correctness: \exists covert information flows? (transitivity of \leq, automation: graph analysis tools)
- Redundancy: \exists sets of subjects and objects with (transitively) equivalent information contents? (antisymmetry of \leq, automation: graph analysis tools)
implement a model: through an automatically generated, isomorphic ACM(using already-present ACLs!)

Multilevel Security (MLS)

Motivation

Introducing a hierarchy of information flow classes: levels of trust
Subjects and objects are classified:
- Subjects w.r.t. their trust worthiness
- Objects w.r.t. their criticality
Within this hierarchy, information may flow only in one direction \rightarrow "secure" according to these levels!
\rightarrow \exists MLS models for different security goals!

Modeling Confidentiality Levels

Class set: levels of confidentiality e.g. C=\{public,confidential,secret\}
Dominance relation: hierarchy between confidentiality levels e.g. \{public \leq confidential,confidential \leq secret\}
Classification of subjects and objects: cl:S\cup O\rightarrow C e.g. cl(BulletinBoard)=public,cl(Timetable)=confidential
Note: In contrast du Denning, \leq in MLS models is a total order.

Example

Lattice \langle\{public,confidential,secret\},\leq\rangle where \leq=\{\langlepublic,confidential\rangle,\langleconfidential,secret\rangle\}
Objects O=\{ProjectXFiles, Timetable, BulletinBoard\}
Subjects S=\{Ann, Bob\}
Classification of objects (classification level):
- cl(ProjectXFiles)=secret
- cl(Timetable)=confidential
- cl(BulletinBoard)=pulic
Classification of subjects (clearance level):
- cl(Ann)=confidential
- cl(Bob)=public
Neither Ann nor Bob can readProjectXFiles
Ann can
- write to ProjectXFiles and Timetable
- read from Timetable and BulletinBoard
Bob can
- write to all objects
- read from BulletinBoard

The Bell-LaPadula Model

Goal: MLS-Model for Preserving Information Confidentiality

Incorporates impacts on model design ...

from the application domain: hierarchy of trust
from the Denning model: information flow and lattices
from the MLS models: information flow hierarchy
from the HRU model:
- Modeling dynamic behavior: state machine and STS
- Model implementation: ACM
\rightarrow application-oriented model engineering by composition of known abstractions

Idea:

entity sets S,O
lattice\langleC,\leq\rangle defines information flows by
- C: classification/clearance levels
- \leq: hierarchy of trust
classification function cl assigns
- clearance level from C to subjects
- classification level from C to objects
Model’s runtime behavior is specified by a deterministic automaton

BLP Security Model

A BLP model is a deterministic automaton \langleS,O,L,Q,\sum,\sigma,q_0,R\rangle where

S and O are (static) subject and object sets,

L=\langleC,\leq\rangle is a (static) lattice consisting of

the classes set C,

the dominance relation \leq,

Q=M\times CL is the state space where

M=\{m|m:S\times O\rightarrow 2^R\} is the set ofpossible ACMs,

CL=\{cl|cl:S\cup O\rightarrow C\} is a set offunctions that classify entities in S\cup O,

\sum is the input alphabet,

\sigma:Q\times \sum\rightarrow Q is the state transition function,

q_0\in Q is the initial state,

R=\{read,write\} is the set of access rights.

Interpretation

S,O,M,\sum,\sigma,q_0,R: same as HRU
L: models confidentiality hierarchy
cl: models classification meta-information about subjects and objects
Q=M\times CL models dynamic protection states; includes
- rights in the ACM,
- classification of subjects/objects,
- not: S and O (different to HRU \rightarrow consequences for safety analysis?)
Commands in the STS may therefore
- change rights in the ACM,
- reclassify subjects and objects.

Lattice vs. ACM

Given an exemplary BLP model where

S=\{s_1,s_2\}, O=\{o_1,o_2\}
C=\{public,confidential\}
\leq=\{\langlepublic,confidential\rangle\}
cl(s_1)=cl(o_1)=public, cl(s_2)=cl(o_2)=confidential
Observation: L and m are isomorphic \rightarrow redundancy?
\rightarrow So, why do we need both model components?

Rationale

L is an application-oriented abstraction
- Supports convenient for model specification
- Supports easy model correctness analysis (\rightarrow reachability analyses in graphs)
- \rightarrow easy to specify and to analyze
m can be directly implemented by standard OS/DBIS access control mechanisms (ACLs, Capabilities) \rightarrow easy to implement
m is determined (= restricted) by L and cl, not vice-versa!

Rationale for L and m

L and cl control m

m provides an easy specification for model implementation

Consistency of L,cl, and m

We know: IF rules specificed by L and cl are implemented by an ACM m...

So: What are the conditions for m to be a correct representation of L and cl?

Intuition: An ACM m is a correct representation of a lattice L iff information flows granted by m do not exceed those defined by L and cl. \rightarrow BLP security property

Consequence: If we can prove this property for a given model, then its implementation (by m) is consistent with the rules given by L and cl.

BLP Security

Help Definitions

Read-Security Rule A BLP model state \langlem,cl\rangle is called read-secure iff \forall s\in S,o\in O:read\in m(s,o)\Rightarrow cl(o) \leq cl(s).

Write-Security Rule A BLP model state \langlem,cl\rangle is called write-secure iff \forall s\in S,o\in O:write\in m(s,o)\Rightarrow cl(s)\leq cl(o).

Note: In some literature, read-security is called "simple security", while write-security is called "^*-property". Reasons are obscure-historical.

State Security A BLP model state is called secure iff it is both read- and write-secure.

Model Security A BLP model with initial state q_0 is called secure iff

q_0 is secure and

each state reachable from q_0 by a finite input sequence is secure.

The above definition is

intuitive
difficult to verify: state reachability...

Auxiliary Definition: The Basic Security Theorem for BLP (BLP BST)

A convenient tool for proving BLP security
Idea: let’s look at properties of the finite and small model components \rightarrow\sigma\rightarrow STS

The BLP Basic Security Theorem

A BLP model \langleS,O,L,Q,\sum,\sigma,q_0,R\rangle is secure iff both of the following holds:

q_0 is secure

\sigma is build such that for each state q reachable from q_0 by a finite input sequence, where q=\langlem,cl\rangle and q′=\sigma(q,\delta)=m′,cl′,\forall s\in S, o\inO,\delta\in\sum the following holds:

Read-security conformity:

read \not\in m(s,o)\wedge read\in m′(s,o)\Rightarrow cl′(o)\leq cl′(s)

read \in m(s,o) \wedge\lnot (cl′(o)\leq cl′(s)) \Rightarrow read \not\in m′(s,o)

Write-security conformity:

write \not\in m(s,o)\wedge write \in m′(s,o)\Rightarrow cl′(s)\leq cl′(o)

write \in m(s,o)\wedge\lnot(cl′(s)\leq cl′(o)) \Rightarrow write \not\in m′(s,o)

Proof of Read Security

Technique: Term rewriting Let q=\sigma*(q_0 ,\sigma^+),\sigma^+\in\sigma^+,q′=\delta(q,\sigma),\sigma\in\sigma,s\in S,o\in O. With q=\langlem,cl\rangle and q′=m′,cl′, the BLP BST for read-security is
- (a1) read \not\in m(s,o) \wedge read\in m′(s,o) \Rightarrow cl′(o) \leq cl′(s)
- (a2) read \in m(s,o) \wedge\lnot (cl′(o)\leq cl′(s)) \Rightarrow read \not\in m′(s,o)
- Let’s first introduce some convenient abbreviations for this:
  - R:=read\in m(s,o)
  - R′:=read\in m′(s,o)
  - C′:=cl′(o) \leq cl′(s)
  - \sigma^+ is the set of finite, non-empty input sequences.
Proposition: (a1) \wedge (a2)\equiv read-security
Proof: (a1) \wedge (a2)= R′ \Rightarrow C′\equiv read\in m′(s,o) \Rightarrow cl′(o)\leq cl′(s), which exactly matches the definition of read-security for q′.
Write-security: Same steps for (b1)\wedge (b2).

Where Do We Stand?

Precision: necessary and sufficient conditions for BLP security property
Analytical power: statements about dynamic model behavior based on static analysis of the (finite and generally small) STS \rightarrow tool support
Insights: shows that BLP security is an inductive property

Problem: Larger systems: only source of access rules is the trust hierarchy \rightarrow too coarse-grained!

Idea: Encode an additional, more fine-grained type of access restriction in the ACM \rightarrow compartments

Comp: set of compartments
co:S\cup O\rightarrow 2^{Comp}: assigns a set of compartments to an entity as an (additional) attribute
Refined state security rules:
- \langlem,cl,co\rangle is read-secure \Leftrightarrow\forall s\in S,o\in O:read \in m(s,o)\Rightarrow cl(o)\leq cl(s)\wedge co(o) \subseteq co(s)
- \langlem,cl,co\rangle is write-secure \Leftrightarrow\forall s\in S,o\in O:write\in m(s,o)\Rightarrow cl(s)\leq cl(o)\wedge co(o) \subseteq co(s)
- Good ol’ BLP: \langleS,O,L,Q,\sigma,\delta,q_0\rangle
- With compartments: $\langleS,O,L,Comp,Q_{co},\sigma,\delta,q_0\rangle where Q_{co}=M\times CL\times CO and CO=\{co|co:S\cup O\rightarrow 2^{Comp}\}

Example

Let co(o)=secret,co(o)=airforce
s_1 where cl(s_1)=public,co(s_1)=\{airforce,navy\} can write o
s_2 where cl(s_2)=secret,co(s_2)=\{airforce,navy\} can read and write o
s_3 where cl(s_3)=secret,co(s_3)=\{navy\} can do neither

BLP Model Summary

Model Achievements

Application-oriented modeling \rightarrow hierarchical information flow (goal: preserve confidentiality)
Scalability \rightarrow attributes: trust levels
Modeling dynamic behavior \rightarrow automaton with STS
Correctness guarantees
- Of model specification: analysis of
  - consistency: BLP security, BST
  - completeness of IF: IFG path finding
  - presence of unintended, transitive IF: IFG path finding
  - unwanted redundancy: IF cycles \rightarrow information equivalence classes
  - safety properties:decidable!
  - \rightarrow tool-supportpossible!
- Of model implementation: good ol’ ACM \rightarrow ACLs, capabilities
Implementation
- ACM is a standard AC mechanism in contemporary implementation platforms (cf. prev. slide)
- Contemporary standard OSs need this: do not support mechanisms for
  - entity classification
  - arbitrary STSs
- \rightarrow newer platforms may do: SELinux, SEAndroid, TrustedBSD, Solaris, Trusted Extensions, PostgreSQL
Is an example of a hybrid model: IF + AC + ABAC

Lessons Learned - What we can learn from BLP for designing and using security models:

Model composition from known model abstractions
- Denning: IF modeling
- ABAC: IF classes and compartments as attributes
- MSL: modeling trust as a linear hierarchy
- HRU: modeling dynamic behavior
- ACM: implementing application-oriented policy semantics
Consistency is an important property of composed models
BLP is further extensible and refinable \rightarrow starting point for later models, e. g. Biba

The Biba Model

BLP upside down [Biba, 1977]:

BLP \rightarrow preserves confidentiality
Biba \rightarrow preserves integrity

Applications Example: On-board Airplane Passenger Information Systems

Goal: Provide in-flight information in cabin network
- Flight instruments data
- Outboard camera video streams
- communication pilot - tower
Integrity: no information flow from cabin to flight deck!
As employed in Boeing 787: common network for cabin and flight deck + software firewall + Biba implementation

Windows Vista UAC

An application of the Biba model for OS access control:
Integrity: Protect system files from malicious user (software) tampering
Class hierarchy:
- system: OS level objects
- high: services
- medium: user level objects
- low: untrusted processes e. g. web browser, setup application, ...
Consequence: every file, process, ... created by the web browser is classified low \rightarrow cannot violate integrity of system- and user-objects
Manual user involvement (\rightarrow DAC portion of the policy):resolving intended exceptions, e. g. to install trusted application software

Non-interference Models

Problem No. 1: Covert Channels

Covert Channel [Lampson, 1973] Channels [...] not intended for information transfer at all, such as the service program’s effect on the system load.

AC policies (ACM, HRU, TAM, RBAC, ABAC): colluding malware agents, escalation of common privileges
- Process 1: only read permissions on user files
- Process 2: only permission to create an internet socket
- both:communication via covert channel(e. g. swapping behavior)
MLS policies (Denning, BLP, Biba): indirect information flow exploitation (Note: We can never prohibitany possible transitive IF ...)
- Test for existence of a file
- Volume control on smartphones
- Timing channels from server response times

Problem No. 2: Damage Range How to substantiate a statement like: "Corruption of privileged system software will never have any impact on other system components." \rightarrow Attack perimeter

Idea of NI models:

Once more: higher level of abstraction
Policy semantics: which domains should be isolated based on their mutual impact

Consequences:

Easier policy modeling
More difficult policy implementation ...(\rightarrow higher degree of abstraction!)

Example 1: Multi-application Smart Cards

Different services, different providers, different levels of trust
Shared resources: Runtime software, OS, hardware (processor, memory, I/O interfaces, ...)
Needed:Total isolation of services (program code, security-critical information e. g. private keys)
\rightarrow Guarantee of total non-interference between domains

Example 2: Server System

Different services: web hosting, mail service, file sharing
Shared resources (see example 1)
Needed:Precisely defined and restricted cross-domain interactions (e. g. file up-/downloads, socket communication, shared memory read/write, ...)
\rightarrow Guarantee of limited non-interferenc ebetween domains

NI Security Policies

NI-Policies Specify

Security domains
Cross-domain (inter)actions \rightarrow interference From convert channels to domain interference:

Non-Interference

Two domains do not interfere with each other iff no action in one domain can be observed by the other.

\rightarrow NI Model Abstractions:

Set of domains D
A non-interference relation ≈_{NI}\subseteq D\times D, such that d_1 ≈_{NI} d_2\Leftrightarrow d_1 does not interfere with d_2
Subjects executeactions a\in A
Effects of actions on domains defined by a mapping dom:A\rightarrow 2^D

NI Security Model An NI model is a det. automaton \langleQ,\sigma,\delta,\lambda,q_0,D,A,dom,≈_{NI},Out\rangle where

Q is the set of (abstract) states,

\sigma=A is the input alphabet where A is the set of (abstract) actions,

\delta:Q\times\sigma\rightarrow Q is the state transition function,

\lambda:Q\times\sigma\rightarrow Out is the output function,

q_0\in Q is the initial state,

D is a set of domains,

dom:A\rightarrow 2^D is adomain function that completely defines the set of domains affected by an action,

≈_{NI}\subseteq D\times D is a non-interference relation,

Out is a set of (abstract) outputs.

NI Security Model is also called Goguen/Meseguer-Model [Goguen and Meseguer, 1982].

BLP written as an NI Model

BLP Rules:
- write in class public may affect public and confidential
- write in class confidential may only affect confidential
NI Model:
- D=\{d_{pub},d_{conf}\}
- write in d_{conf} does not affect d_{pub}, so d_{conf} ≈_{NI} d_{pub}
- A=\{writeInPub, writeInConf\}
- dom(writeInPub)=\{d_{pub},d_{conf}\}
- dom(writeInConf)=\{d_{conf}\}

NI Model Analysis

Goal

AC models: privilege escalation (\rightarrow HRU safety)
BLP models:model consistency (\rightarrow BLP security)
NI models:Non-interference between domains

Non-Interference Intuitively: Is there a sequence of actions a^*\in A^* that violates ≈_{NI}? \rightarrow A model is called $NI$-secure iff there is no sequence of actions that results in an illegal domain interference. Now what does this meansprecisely...?

Before we define what NI-secure is, assume we could remove all actions from an action sequence that have no effect on a given set of domains:

Purge Function

Let aa^*\in A^* be a sequence of actions consisting of a single action a\in A\cup\{\epsilon\} followed by a sequence a^*\in A^*, where \epsilon denotes an empty sequence. Let D′\in 2^D be any set of domains. Then, purge: A^*\times 2^D \rightarrow A^* computes a subsequence of aa^* by removing such actions without an observable effect on any element of $D′:

purge(aa^*,D′)=\begin{cases} a\circ purge(a^*,D′), \quad\exists d_a\in dom(a),d′\in D′:d_a≈_I d′ \\ purge(a^*,D′), \quad\text{ otherwise }\end{cases}

$purge(\epsilon,D′)=\epsilon$ where ≈_I is the complement of ≈_{NI}:d_1 ≈_I d_2\Leftrightarrow \lnot(d_1 ≈_{NI} d_2).

NI Security

For a state q\in Q of an NI model \langleQ,\sigma,\delta,\lambda,q_0,D,A,dom,≈_{NI},Out\rangle, the predicate ni-secure(q) holds iff \forall a\in A,\forall a^*\in A^*:\lambda (\delta^*(q,a^*),a)=\lambda(\delta^*(q,purge(a^*,dom(a))),a)

Interpretation

Running an NI model on \langleq,a^*\rangle yields q′=\delta^*(q,a^*).
Running the model on the purged input sequence so that it contains only actions that, according to ≈_{NI}, actually have impact on dom(a) yields q′_{clean}=\delta^*(q,purge(a^*,dom(a)))
If \forall a\in A:\lambda(q′,a)=\lambda(q′_{clean},a), than the model is called NI-secure w.r.t. q(ni-secure(q)).

Comparison to HRU and IF Models

HRU Models
- Policies describe rules that control subjects accessing objects
- Analysis goal: right proliferation
- Covert channels analysis: only based on model implementation
IF Models
- Policies describe rules about legal information flows
- Analysis goals: indirect IFs, redundancy, inner consistency
- Covert channel analysis: same as HRU
NI Models
- Rules about mutual interference between domains
- Analysis goal: consistency of ≈_{NI} and dom
- Implementation needs rigorous domain isolation (more rigorous than MLS, e.g. object encryption is not sufficient!) \rightarrow expensive
- State of the Art w.r.t. isolation completeness: VMs > OS domains (SELinux) > Containers

Hybrid Models

Real-world Scenarios e.g. workflow modeling: IBAC plus RBAC plus IF plus time... \rightarrow Hybrid models by composing pure models

Chinese-Wall Policies

Security policy family for consulting companies

Clients of any such company
- Companies, including their business data
- Often: mutual competitors
Employees of consulting companies
- Are assigned to clients they consult (decided by management)
- Work for many clients \rightarrow gather insider information
\rightarrow Policy goal: No flow of (insider) information between competing clients

Why look at specifically these policies?

Modeling
- Composition of
  - Discretionary IBAC components
  - Mandatory ABAC components
- Driven by real-world demands: iterative refinements of a model over time
  - Brewer-Nash model [Brewer and Nash, 1989]
  - Information flow model [Sandhu, 1992a]
  - Attribute-based model [Sharifi and Tripunitara, 2013]
Application areas: consulting, cloud computing

The Brewer-Nash Model

Specialized model: Explicitly tailored towards Chinese Wall (CW) policies

Model Abstractions

Consultants represented by subjects
Client companies represented by objects, which comprise a company’s business data
Modeling of competition by conflict classes: two different clients are competitors \Leftrightarrow their objects belong to the same class
No information flow between competing objects \rightarrow a "wall" separating any two objects from the same conflict class
Additional ACM for refined management settings of access permissions

Example

Consultancy clients
- Banks: HSBC, Deutsche Bank, Citigroup
- Oil companies: Shell, Exxon Mobil/Esso
Conflicts: business-crucial information flows between banks and oil companies

Representation of Conflict Classes

Client company data: object set O
Competition: conflict relation C\subseteq O\times O:\langleo,o′\rangle\in C\Leftrightarrow o and o′ belong to competing companies (non-reflexive, symmetric, generally not transitive)
In terms of ABAC:object attribute att_O:O\rightarrow 2^O, such that att_O(o)=\{o′\in O|\langleo,o′\rangle\in C\}.

Representation of a Consultant’s History

Consultants: subject set S
History relation H\subseteq S\times O:\langles,o\rangle\in H\Leftrightarrow s has previously consulted o
In terms of ABAC: subject attribute att_S:S\rightarrow 2^O, such that att_S(s)=\{o\in O|\langles,o\rangle\in H\}.

Brewer-Nash Security Model

The Brewer-Nash model of the CW policy is a det. automaton\langleS,O,Q,\sigma,\delta,q_0,R\rangle where

S and O are sets of subjects (consultants) and (company data) objects,

Q=M\times 2^C\times 2^H is the state space where

M=\{m|m:S\times O\rightarrow 2^R\} is the set ofpossible ACMs,

C\subseteq O\times O is the conflict relation: \langleo,o′\rangle\in C\Leftrightarrow o and o′ are competitors,

H\subseteq S\times O is the history relation: \langles,o\rangle\in H\Leftrightarrow s has previously consulted o,

\sigma=OP \times X is the input alphabet where

OP=\{read,write\} is a set of operations,

X=S \times O is the set of arguments of these operations,

\delta:Q \times\sigma\rightarrow Q is the state transition function,

q_0\in Q is the initial state,

R=\{read,write\} is the set of access rights.

At the time depicted:

Conflict relation: C=\{\langleHSBC,DB\rangle,\langleHSBC,Citi\rangle,\langleDB,Citi\rangle,\langleShell,Esso\rangle\}
History relation: H=\{\langleAnn,DB\rangle,\langleBob,Citi\rangle,\langleBob,Esso\rangle\}

Brewer-Nash STS

Read (here: similar to HRU notation) $command read(s,o)::=if read \in m(s,o) \wedge\forall \langleo′,o\rangle\in C:\langles,o′\rangle\not\in H$ $then$ $H:=H\cup{\langles,o\rangle}$ fi
Write $command write(s,o)::=if write \in m(s,o) \wedge\forall o′\in O:o'\not=o \Rightarrow \langles,o′\rangle\not\in H$ $then$ $H:=H\cup{\langles,o\rangle}$ fi

Not shown: Discretionary policy portion \rightarrow modifications in m to enable fine-grained rights management.

Restrictiveness

Write Command: s is allowed to write o\Leftrightarrow write\in m(s,o)\wedge\forall o′\in O:o′\not=o\Rightarrow\langles,o′\rangle\not\in H
Why so restrictive? \rightarrow No transitive information flow!
- \rightarrow s must never have previously consulted any other client!
- \Rightarrow any consultant is stuck with her client on first read access
- \Rightarrow not (yet) a professional model!

Brewer-Nash Model

Instantiation of a Model

Initial State q_0
- m_0: consultant assignments to clients, issued by management
- C_0: according to real-life competition
- H_0 =\varnothing

Secure State $\forall o,o′ \in O,s\in S:\langles,o\rangle\in H_q\wedge\langles,o′\rangle\in H_q\Rightarrow\langleo,o′\rangle\not\in C_q$ Corollary: \forall o,o′\in O,s\in S:\langleo,o′\rangle\in C_q\wedge\langles,o\rangle\in H_q\Rightarrow \langles,o′\rangle\not\in H_q

Secure Brewer-Nash Model Similar to "secure BLP model".

In the exercises: STS, transformation into pure HRU calculus, dynamic subject and object sets.

Summary Brewer-Nash

What’s remarkable with this model?

Composes DAC and MAC components
Simple model paradigms
- Sets (subjects, objects)
- ACM (DAC)
- Relations (company conflicts, consultants history)
- Simple "read" and "write" rule
- \rightarrow easy to implement
Analysis goals
- MAC: Model security
- DAC: safety properties
Drawback: Restrictive write-rule

Professionalization

Remember the difference: trusting humans (consultants) vs. trusting software agents (subjects)
- Consultants are assumed to be trusted
- Systems (processes, sessions, etc.) may fail, e. g. due to a malware attack
\rightarrow Write-rule applied not to humans, but to (shorter-lived) software agents \rightarrow mitigating malware effectiveness
\rightarrow Subject set S models consultant’s subjects (e. g. processes) in a group model:
- All processes of one consultant form a group
- Group members
  - have the same rights in m
  - have individual histories
  - are strictly isolated w.r.t. IF
Solution approach: as we already know \rightarrow model refinement!

The Least-Restrictive-CW Model

Restrictiveness of Brewer-Nash Model:

If \langleo_i,o_k\rangle\in C: no transitive information flow o_i \rightarrow o_j\rightarrow o_k, i.e. consultant(s) of o_i must never write to any o_j\not=o_i
This is actually more restrictive than necessary: o_j\rightarrow o_k and afterwards o_i\rightarrow o_j would be fine! (no information can actually flow from o_i to o_k)
In other words: Criticality of an IF depends on existence of earlier flows.

Idea LR-CW[Sharifi and Tripunitara, 2013]: Include time as a model abstraction!

Approach:

\forall s\in S,o\in O: remember, which information has flown to an entity
\rightarrow subject-/object-specific history, $≈$attributes ("lables")

LR-CW Model

The Least-Restrictive model of the CW policy is a deterministic automaton \langleS,O,F,\zeta,Q,\sigma,\delta,q_0\rangle where

S and O are sets of subjects (consultants) and data objects,

F is the set of client companies,

\zeta:O\rightarrow F ("zeta") is a function mapping each object to its company,

Q=2^C \times 2^H is the state space where

C\subseteq F\times F is the conflict relation: \langlef,f′\rangle\in C\Leftrightarrow f and f′ are competitors,

H=\{Z_e\subseteq F|e\in S\cup O\} is the history set: f\in Z_e\Leftrightarrow e contains information about f(Z_e is the "history label" of e),

\sigma=OP\times X is the input alphabet where

OP=\{read,write\} is the set of operations,

X=S\times O is the set of arguments of these operations,

\delta:Q\times\sigma\rightarrow Q is the state transition function,

q_0\in Q is the initial state

At the time depicted (before the first write):
- Client companies: F=\{HSBC,DB,Citi,Shell,Esso\}
- History set: H=\{Z_{Ann},Z_{Bob},Z_{o1} ,...,Z_{o|O|}\} with history labels
  - Z_{Ann}=\{DB\}
  - Z_{Bob}=\{Citi,Esso\},
  - Z_{oi}=\{\zeta(o_i)}, 1\leq i\leq |O|.

Inside the STS

a reading operation
- requires that no conflicting information is accumulated in the subject potentially increases the amount of information in the subject
- command read(s,o) ::= if \forall f,f′\in Z_s \cup Z_o:\langlef,f′\rangle\not\in C then Z_s:=Z_s\cup Z_o fi
a writing operation
- requires that no conflicting information is accumulated in the object potentially increases the amount of information in the object
- command write(s,o) ::= if \forall f,f′\in Z_s\cup Z_o:\langlef,f′\rangle\not\in C then Z_o:=Z_o\cup Z_s fi

Model Achievements

Applicability: more writes allowed in comparison to Brewer-Nash (note that this still complies with the general CW policy)
Paid for with
- Need to store individual attributes of all entities (their history labels Z_e)
- Dependency of write permissions on earlier actions of other subjects
More extensions:
- Operations to modify conflict relation
- Operations to create/destroy entities

An MLS Model for Chinese-Wall Policies

Problems

Modeling of conflict relation
Modeling of consultants history

Conflict relation is

non-reflexive: no company is a competitor of itself
symmetric: competition is always mutual
not necessarily transitive: any company might belong to more than one conflict class \Rightarrow if a competes with b and b competes with c, then a and c might still be in different conflict classes (= no competitors) \rightarrow Cannot be modeled by a lattice!

Reminder:In a lattice$\langleC,\leq\rangle$,$\leq$ is a partial order:

reflexive (\forall a\in C:a \leq a)
anti-symmetric (\forall a,b \in C:a \leq b \wedge b \leq a\Rightarrow a=b)
transitive (a,b,c \in C:a \leq b \wedge b \leq c \Rightarrow a \leq c)

MLS-CW Example:

Two conflict classes:
Resulting valid information flows:
Problem: How to express this more directly, by allowed information flows rather than (forbidden) conflicts?

Idea: Labeling of entities

Class of an entity (subject or object) reflects information it carries
Consultant reclassified whenever a company data object is read
\rightarrow Classes and labels:
Class set of a lattice C=\{DB,Citi,Shell,Esso\}
Entity label: vector of information already present in each business branch (formerly known as conflict classin Brewer-Nash!)
In our example, a vector consists of 2 elements \in C; resulting in labels such as:
- [\epsilon,\epsilon] (exclusively for inf_C)
- [DB,\epsilon] (for DB-objects or -consultants)
- [DB,Shell] (for subjects or objects containing information from both DB and Shell)
- [Esso,Shell] (illegal label!)
- ...

Summary CW

Why is the "Chinese Wall" policy interesting?

One policy, multiple models:
- The Brewer-Nash model demonstrates hybrid DAC-/MAC-/IFC-approach
- The Least-Restrictive CW model demonstrates a more practical professionalization
- The MLS-CW model demonstrates applicability of lattice-based IF modeling \rightarrow semantically cleaner approach
Applications: Far beyond traditional consulting scenarios...$\rightarrow$ current problems in cloud computing!

Summary

Security Models

Formalize informal security policies for the sake of
- objectification by unambiguous calculi
- explanation and (possibly) proof of security properties (e.g. HRU safety, BLP security, NI security) by formal analysis techniques
- foundation for correct implementations
Are composed of simple building blocks
- E.g. ACMs, sets, relations, functions, lattices, state machines
- ... that are combined and interrelated to form more complex models
- \rightarrow (D)RBAC, (D)ABAC, BLP, Brewer-Nash, LR-CW, MLS-CW

Remember: Goals of Security Models

Unambiguous policy formalization to

reason about policy correctness
correctly implement a policy

Practical Security Engineering

Problem: Off-the-shelf models not always a perfect match for real-world scenarios

Goal: Design of new, application-specific models

Identify common components found in many models \rightarrow generic model core
Core specialization
Core extension
Glue between model components

Model Engineering

Model Family

What we have

In Formal Words ...

HRU: \langle Q, \sum , \delta, q_0 , R \rangle
DRBAC_0 : \langle Q, \sum , \delta, q_0 , R, P, PA \rangle
DABAC: \langle A , Q ,\sum , \delta, q_0 \rangle
TAM: \langle Q , \sum , \delta, q_0 , T, R \rangle
BLP: \langle S, O, L, Q , \sum , \delta, q_0 , R \rangle
NI: \langle Q , \sum , \delta, \lambda ,q_0 , D, A, dom, =_{NI} , Out \rangle

Core Model (Common Model Core)

HRU: \langle Q, \sum , \delta, q_0 , \not R \rangle
DRBAC_0 : \langle Q, \sum , \delta, q_0 , \not R, \not P, \not PA \rangle
DABAC: \langle \not A , Q ,\sum , \delta, q_0 \rangle
TAM: \langle Q , \sum , \delta, q_0 , \not T, \not R \rangle
BLP: \langle \not S, \not O, \not L, Q , \sum , \delta, q_0 , \not R \rangle
NI: \langle Q , \sum , \delta, \not \lambda ,q_0 , \not D, \not A, \not dom, \not =_{NI} , \not Out \rangle
\rightarrow \langle Q ,\sum , \delta, q_0 \rangle

Core Specialization

HRU: \langle Q, \sum , \delta, q_0 , R \rangle \Rightarrow Q = 2^S \times 2^O \times M
DRBAC_0 : \langle Q, \sum , \delta, q_0 , R, P, PA \rangle \Rightarrow Q = 2^U\times 2^{UA}\times 2^S \times USER \times ROLES
DABAC: \langle A , Q ,\sum , \delta, q_0 \rangle \Rightarrow Q = 2^S\times 2^O \times M\times ATT
TAM: \langle Q , \sum , \delta, q_0 , T, R \rangle \Rightarrow Q = 2^S\times 2^O\times TYPE \times M
BLP: \langle S, O, L, Q , \sum , \delta, q_0 , R \rangle \Rightarrow Q = M \times CL
NI: \langle Q , \sum , \delta, \lambda ,q_0 , D, A, dom, =_{NI} , Out \rangle

Core Extensions

HRU: \langle Q, \sum , \delta, q_0 , R \rangle \Rightarrow R
DRBAC_0 : \langle Q, \sum , \delta, q_0 , R, P, PA \rangle \Rightarrow R,P,PA
DABAC: \langle A , Q ,\sum , \delta, q_0 \rangle \Rightarrow A
TAM: \langle Q , \sum , \delta, q_0 , T, R \rangle \Rightarrow T,R
BLP: \langle S, O, L, Q , \sum , \delta, q_0 , R \rangle \Rightarrow S,O,L,R
NI: \langle Q , \sum , \delta, \lambda ,q_0 , D, A, dom, =_{NI} , Out \rangle \Rightarrow \lambda,D,A,dom,=_{NI},Out
\rightarrow R, P, PA, A , T , S , O , L , D , dom , =_{NI} , ...

Glue

E.g. TAM: State transition scheme (types)
E.g. DABAC: State transition scheme (matrix and predicates)
E.g. Brewer/Nash Chinese Wall model: "$\wedge$" (simple, because H+C\not= m)
E.g. BLP
- BLP read rule
- BLP write rule
- BST
- (much more complex, because rules restrict m by L and cl )

\rightarrow Model Engineering Principles

Core model
Core specialization, e.g.
Q = 2^S\times 2^O \times M (HRU)
Q = M\times CL (BLP)
Core extension, e.g.
- e.g. L (BLP)
- T (TAM)
- D, dom ,=_{NI} (NI)
Component glue, e.g.
- Chinese Wall: DAC "$\wedge$" MAC in AS
- BLP: complex relation between ACM and lattice
- \rightarrow BLP security, BLP BST

You should have mastered now: A basic tool set for model-based security policy engineering

A stock of basic security model abstractions
- ACFs and ACMs
- Model states and transitions defined by an STS
- Attributes (roles, confidentiality classes, information contents, location, ...)
- Information flows
A stock of formal model building blocks
- Sets, functions, relations
- Deterministic automatons
- Graphs and lattices
A stock of standard, off-the-shelf security models
Methods and techniques
- for model-based proof of policy properties properties
- for combining basic model building blocks into new, application-oriented security models

Model Specification

Policy Implementation

We want: A system controlled by a security policy
We have: A (satisfying) formal model of this policy

To Do

How to convert a formal model into an executable policy?
- \rightarrow Policy specification languages
How to enforce an executable policy in a system?
- \rightarrow security mechanisms and architectures (Chapters 5 and 6)

Role of Specification Languages: Same as in software engineering

To bridge the gap between
- Abstractions of security models (sets, relations, ...)
- Abstractions of implementation platforms (security mechanisms such as ACLs, krypto-algorithms, Security Server ...)
Foundation for
- Code verification
- Or even more convenient: Automated code generation

Approach

Abstraction level:
- Step stone between model and security mechanisms
- \rightarrow More concrete than models
- \rightarrow More abstract than programming languages ("what" instead of "how")
Expressive power:
- Domain-specific; for representing security models only
- \rightarrow Necessary: adequate language paradigms
- \rightarrow Sufficient: not more than necessary (no dead weight)

Domains

Model domain
- e.g. AC models (TAM, RBAC, ABAC)
- e.g. IF models (MLS)
- e.g. NI models
Implementation domain
- OS
- Middleware
- Applications

DYNAMO: A Dynamic-Model-Specification Language

formerly known as "CorPS: Core-based Policy Specification Language"

Language Domain: RBACmodels

RBAC 0 - 3
DRBAC 0 - 3
DABAC (with some restrictions)

Language Paradigms: Abstractions of (D)RBAC models

Users, roles, permissions, sessions
State transition scheme (STS)

Language Features: Re-usability and inheritance

Base Classes: Model family (e.g. DRBAC_0 , DRBAC_1 , ...)
- Model components
- Conditions
- Primitives
Policy Classes : Inherit definitions from Base Classes
- State space
- State transition scheme
- Extensions

Tools

DYNAMO compiler("corps2cpp"): Translates specification into
- XML \rightarrow analysis by WORSE algorithms
- C++ classes \rightarrow integration into TCB (OS/Middleware/Application)

Example: Specification of a DRBAC_0 Model

$DRBAC_0 = RBAC_0 + Automaton \rightarrow$ $RBAC_0 = \langle U , R , P , S , UA , PA , user , roles \rangle$ $DRBAC_0 = \langle Q , \sum, \delta, q_0 , R , P , PA \rangle$ Q = 2^U \times 2^S \times 2^{UA}\times ...

Policy Specification in EBNF

policy =
  "begin" "policy" string ":" string ":"
    "state-space" ":" "{" state_space "}" ";"     // Q
    "input-vector" ":" "{" input_vector "}" ";"   // SUM
    "begin" "authorisation-scheme" ":"            // delta
      authorisation_scheme
    "end" "authorisation-scheme" ";"              // q_0
    "begin" "initial-state" ":"
      inital_state
    "end" "initial-state" ";"
    "begin" "extension-vector" ":"                // E
      extension_vector
    "end" "extension-vector" ";"
"end" "policy" ";"

OpenMRS: A DRBAC_0 Policy Specification Example

begin policy openRMS: DRBAC_0 :
  state-space : {U, O, S, UA, PA, user, roles};
  input-vector : {OP, U, O, S , R};
  begin authorisation-scheme :
    read_medical_record(U u, O o, OP op):
      condition : f_rbac(u, o, ’read_medical_record’);
    write_medical_record(U u, O o, OP op):
      condition : f_rbac(u, o, ’write_medical_record’);
    activate_role_in_session(S s, R r):
      condition : can_activate_role(s, r);
      begin body :
        activate_role(s, r);
      end body ;
  end authorisation-scheme ;
  begin initial-state :
    U = {’Cox’, ’Reid’};
    O = {‘epr1’, ‘epr2’, ...}; S = {‘s1’, ‘s2’};
    UA = {[’Cox’, ’doctor’], [’Reid’, ’doctor’]};
    user = {(’s1’ : ‘Cox’), (‘s2’ : ’Reid’)}; ...
  end initial-state;
  begin extension-vector :
    OP = {’read_medical_record’, ’write_medical_record’, ’activate_role_in_session’};
    R = {’doctor’, ’nurse’, ’researcher’};
  end extension-vector ;
end policy ;

What Can We Do Now? DYNAMO specification \rightarrow corps2cpp \rightarrow C++-Classes \rightarrow Security Server

DYNAMO Summary

Specification language for (D)RBAC security models
Base classes: DRBAC_0 ... DRBAC_3
Conditions
- f_rbac
- can_activate_role
- can_assign_user_to_role
Primitives
- create_user , destroy_user
- create_object , destroy_object
- create_role , destroy_role
- create_session , destroy_session
- activate_role
- ...
Tools
- Compiler: XML, C++ generation

SELinux Policy Language

Language Domain

I/R/A-BAC models
IF models
NI models

Implementation Domain

Operating systems accesscontrol

Language paradigms

OS Abstractions: Users, processes, files, directories, sockets, pipes, ...
I/R/ABAC, TAM, MLS, NI model paradigms: Users, rights, roles, types, attributes, security domains, ...

Tools

Specification: Policy creating and validation
Policy compiler: Translates policy specifications
Security server: Policy runtime environment (RTE) in OS kernel’s security architecture
LSM hooks: Support policy enforcement in OS kernel’s security architecture

Technology

Policy compiler \rightarrow translates specifications into loadable binaries (ACM)
Security architecture \rightarrow implementation of Flask architecture

Fundamental Flask Security Architecture as found in SELinux:

Basic Language Concepts

Definition of types (a.k.a. "domains")
Labeling of subjects (e.g. processes) with "domains" \rightarrow passwd_t: all processes managing passwords
Labeling of objects (e.g. files, sockets) with "types" \rightarrow shadow_t: all files containing password info
AC: defined by permissions between pairs of types ("type enforcement") \rightarrow allow\ passwd_t\ shadow_t:file \{read\ write\}
Dynamic interactions: transitions between domains \rightarrow allow\ user_t\ passwd_t:process {\transition\}

Policy Rules

Grant permissions: allow rules "allow" ":"
Typical domains: user_t, bin_t, passwd_t, insmod_t, tomCat_t, ...
Classes: OS abstractions (process, file, socket, ...)
Permissions: read, write, execute, getattr, signal, transition, ... (≈ 1000)

The Model Behind: 3 Mappings

Classification cl : S\cup O \rightarrow C where C =\{ process , file , dir , pipe, socket , ...\}
Types type: S\cup O \rightarrow T where T =\{ user_t , passwd_t , bin_t , ...\}
Access Control Function ( Type Enforcement , TE ) te : T\times T \times C \rightarrow 2^R
\rightarrow ACM : T\times( T \times C ) \rightarrow 2^R

Idea only: SELinux RBAC

Users and Roles

User ID assigned on login
RBAC rules confine type associations "Only users in role doctor_r may transit to domain $edit-epr_t$"
\rightarrow fine-grained domain transitions
\rightarrow Attributes in SELinux-style RBAC:
- User ID (\not = Linux UID)
- Role ID

What we can do now: Specification \rightarrow Tool \rightarrow Binary \rightarrow Security Server

Summary SELinux Policy Specification Language

Application Domain

OS-level security policies

Model Domain

∗BAC, MLS, NI

Model abstractions

TE: MAC rules based on types
ABAC:MAC rules based on attributes
RBAC: MAC rules based on roles
Additionally: BLP-style MLS

Other Policy Specification Languages

XACML ( eXtensibleAccess Control Markup Language )
NGAC ( Next Generation Access Control Language )
SEAL (Label-based AC policies)
Ponder (Event-based condition/action rules)
GrapPS (Graphical Policy Specification Language)
GemRBAC (Role-based AC models)
PTaCL (Policy re-use by composition)

Summary

Security Models in Practice

Model abstractions
- Subjects, objects, rights
- ACMs and state transition schemes
- Types, roles, attributes
- Information flow, non-interference domains
Model languages
- Sets, functions, relations, lattices/IFGs
- Deterministic automata
Model engineering
- Generic model core
- Core specialization and extension

Next Step: Policy Implementation & Integration

Models expect

Confidentiality, integrity, and authenticity of
- Model entities
- Entity communication
Tamperproofness of policy implementation
Total access mediation for policy

Security Mechanisms

Security Models Implicitly Assume

Integrity of model implementation
Model state
Authorization scheme
Integrity of model operations call
Parameters of authorization scheme ops
Completeness and total mediation of their invocation
AC, IF: no covert chanels
NI: Rigorous domain isolation
... \rightarrow job of the "Trusted Computing Base" (TCB) of an IT system

Definitions

Trusted Computing Base (TCB) The set of functions of an IT system that are necessary and sufficient for implementing its security properties \rightarrow Isolation, Policy Enforcement, Authentication ...

Security Architecture The part of a system’s architecture that implement its TCB \rightarrow Security policies, Security Server (PDP) and PEPs, authentication components, ...

Security Mechanisms Algorithms and data structures for implementing functions of a TCB \rightarrow Isolation mechanisms, communication mechanisms, authentication mechanisms, ...

\rightarrow TCB - runtime environment for security policies

Implementation of Security Policies

(some) TCB functions are integrated in today's commodity OSes
- Isolation
- Subject/object authentication
Complex models (HRU, RBAC, ABAC, MLS) additionally require implementation of
- Authorization schemes
- Roles, lattices, attributes
- \rightarrow stronger concepts and mechanisms
- OS level: Security Server (SELinux, OpenSolaris, TrustedBSD)
- Middleware level: Policy Objects (CORBA, Java, DBMSs, Web Services)
- Application level: user level reference monitors (Flume), user level policy servers (SELinux)

Security mechanisms: A Visit in the Zoo: ...

In OSes
Authenticity
- Of subjects: login
- Of objects: object management, e.g. file systems, communication
Confidentialityand integrity
- Access control lists
In middleware layer (DBMSs, distributed systems)
Authentication
- Authentication server (e.g. Kerberos AS, see below)
- Authentication protocols (e.g. LDAP )
Authorization
- Ticket server (e.g. Kerberos TGS, see below)
In application systems
Authentication
In email servers, Web browsers
In libraries
Confidentiality, integrity, authenticity
- Cryptographic algorithms
- Certificate management for PKIs
In utilities

Confidentiality, integrity, authenticity

Document integrity

dunraven> md5 kap3.pptx
MD5 (kap3.pptx) = 17467e340438ae4dc0e2d9ad9afe
dunraven>

Isolation

Sandboxing in OS X

dunraven> sandbox-exec -f policy fishy-program
sandbox-exec: fishy-program: operation not permitted
dunraven>

\rightarrow security mechanisms are everywhere

Authorization

Lampson, HRU, RBAC, ABAC, BLP, CW \rightarrow ACMs

Security Mechanisms for Implementation?

Access Control Lists und Capability Lists

Lampson’s ACM

We have

Sets S, O and R
ACM m: S\times O\rightarrow 2^R

m	o_2	o_3	o_m
s_1
s_2		{r,w}
s_3	{r,w}
...			{w}
s_n

Properties of an ACM

Large (e.g. "normal" file server: |m| >> 1 TByte)
Sparsely populated
Subject and object identifications in OSes generally are
Not numerical
Not consecutive
Rowsand columns are created and destroyed dynamically
\rightarrow naive ACM implementation such as rightset m[SubjectIdType][ObjectIdType] in a file system‘s metadata (e.g. similar to inode table) out of question

Idea: Distributed ACM Implementation

Split matrix into vectors; options:
- Column vectors
- Row vectors
Attach vectors to subjects resp. objects

Alternative Approaches

Column vectors
- Describe every existing right wrt. an object
- Each vector associated to its object, part of object‘s metadata
- \rightarrow Access control lists (ACLs)
Row vectors
Describe every existing right wrt. a subject
Associated to its subject, part of subject‘s metadata
\rightarrow capability lists

ACLs

Associated to exactly one object
Describes every existing right wrt. object by a set of tuples (subject identification, right set)
Implemented e.g. as list, table, bitmap
Part of object‘s metadata (generally located in inode)

Capability Lists

Associated to exactly one subject
Describes every existing right wrt. subject by a set of tuples (object identification, right set)
Implemented e.g. as list, table, bitmap
Part of object‘s metadata (generally located in process descriptor)

Figuratively

Enrollment: capability list
1. Authorization: high school diploma (personal capability carrying authenticity proof)
2. Authentication: id card
Vienna Opera Ball: ACL
1. Authorization: list of invited people
2. Authentication: (id card)

Operations on ACLs

Create and Delete an ACL

Together with creation and deletion of an object
Options for initialization
Initialrights are create operation parameters
\rightarrow discretionary access control
Initialrights issued by third party
\rightarrow mandatory access control

Example: creat/open of Unix API

S_IRWXU (= 00700oct) user (=owner) has read-, write- and execute permissions
S_IRUSR (= 00400oct) user has read permission
S_IRWXG (= 00070oct) group has read-, write- und execute permissions

Modify an ACL

Add or remove tuples (subject identification, right set)
General Options
- Owner has right to modify ACL
  - \rightarrow implements discretionary access control
- Third party has right to modify ACL
  - \rightarrow implements mandatory access control
- Right to modify ACL is part of ACL
  - \rightarrow universal

Check Rights

Whenever an object is accessed
Search granting tuple in ACL

Negative Rights

Dominate positive rights
Are represented - same as positive rights - by tuples (subject identification, negative rights set)
Rights of a subject s: difference of accumulated positive rights and accumulated negative rights

Example: ACLs in Unix

Metadata (management data) of a file system include

Directories
Inodelist and inodes

Model of a Unix ACL...

	read	write	exec
owner	y	y	n
group	y	n	n
others	n	n	n

3 elements per list list
3 elements per right set
\rightarrow 9 bits
... and its implementation ... coded in 16-bit-word (PDP 11, 1972 !!) ...110 100 000

Operations on Capability Lists

Create and Delete a Capability List

Together with creation and deletion of a subject
Options for initialization
Initial rights same as parent
- $\rightarrow$inherited
Constraints by
- Parent
  - \rightarrow discretionary access control
- Capability
  - \rightarrow mandatory access control

Modification of Capability Lists

Add or remove tuples (object identification, right set)

Passing on Capabilities, options:

Emission and call-back by capability owner
- \rightarrow discretionary access control
Emission and call-back by third party
- \rightarrow mandatory access control
Emission and call-back controlled by capability itself
- \rightarrow universal

Equivalence of ACLs and Capability Lists

Strictly formally speaking: isomorphic
\delta s:
Administration
Distributed systems

`\delta s` in Administration

ACLs - Located near objects

\rightarrow finding all rights of a subject expensive

Example BLP: re-classification of a subject

\rightarrow update every ACL with rights of this subject

Consequence - Group models; e.g.

BLP: subjects with same classification
Unix: subjects belonging to project staff

Capability Lists - Located near objects

\rightarrow finding all rights of an object expensive

Example BLP: re-classification of an object

\rightarrow update every Capability with rights for this object

Consequence - Role models (role: set of rights); e.g.

BLP: set of rights wrt. objects with same classification

`\delta s` in Distributed Systems

Non-distributed Systems: Management and protection of

subject ids and ACLs in trustworthy OS kernel
capability lists in trustworthy OS kernel
Example: Traditional monolithic OS architecture
or in a microkernel architecture
\rightarrow in non-distributed systems,
- Management
- Authentication
- Management and checking of capabilities
- Checking of rights in trustworthy environment

Distributed Systems

No encapsulation of subject ids and ACLs in a single trustworthy OS
\rightarrow in distributed systems,
- Authentication of subjects on subject’s system
- Transfer of subject id via open communication system
- Checking of subject id by object’s system
No encapsulation of capability lists in a single trustworthy OS kernel
\rightarrow in distributed systems,
- Authentication of subjects and management of capabilities on subject’s system
- Transfer of subject id and capabilities via open communication system
- Checking of capabilities and subject ids on object’s system

Vulnerabilities and Counteractions

Subject’s system may fake subject ids
Consequence: Reliable subject authentication required
- \rightarrow authentication architectures (e.g. Kerberos)

\rightarrow in non-distributed systems,

Authentication
Management and checking of capabilities in trustworthy environment

Vulnerabilities and Counteractions

Non-trustworthy subject systems modify capabilities
- \rightarrow cryptographic sealing of capabilities such that
- Issuer can be determined
- Modification can be detected
- sealing e.g. by digital signatures (see below)
Non-trustworthy subject systems pass capabilities to third parties, or Capabilities are copied by third parties while in transit
- \rightarrow personalized capabilities
Exploit stolen capabilities by ntw. subject system by forging subject id
- \rightarrow cryptographically sealed personalized capabilities
- \rightarrow reliable subject authentication required
  - \rightarrow authentication architectures

On the Expressive Power of ACLs and Capability Lists

ACLs and capability lists

Efficient data structures for implementing ACMs
located in
OSs, middleware, DBMSe, application systems
Correctness, tamperproofness, total S/O interaction mediation vital for enforcing access control
\rightarrow implementation by strong architectural principles, see below
Assume reliable authentication of subjects and objects
\rightarrow support by further security mechanisms
Not sufficient for implementing more complex security policies
Authorization schemes

Interceptors

Problem: Implementing authorization schemes

Examples
Open university scenario: "After accessing the solution, assignments can no longer be submitted"
Unix fork : "A child process inherits all security attributes from its parent"
ACLs and capability lists are just lists...

Idea: Policy implementation by algorithms instead of lists

Interceptors

Tamperproof runtime environments for security policies
In total control of subject/object interactions
Observation
Modification
Prevention

General Architectural Principle: Separation of

(Replaceable) strategies
(Strategy-independent) mechanisms

Applied to Interceptors \rightarrow 2 Parts

Runtime environment for security policies (strategies)
- often called "policy decision point" (PDP)
Interception points (mechanisms)
often called "policy enforcement points" (PEP)

Summary Interceptors

RTE for security policies in policy-controlled systems
SELinux: "Policy Server"
CORBA: "Policy Objects"
Architecture: separation of responsibilities
Strategic component
- State; e.g.
  - HRU models: Q= 2^S\times 2^O\times M
  - MLS models: Q =M\times CL
- Algorithms
  - authorization scheme
Mechanisms component
Policy enforcement: total policy entities interaction mediation
Generality: implement - apart from AC policies - a broad scope of policies (all that generally are computable)
"Only documents signed by X may be classified as ‘public’" - \rightarrow rules based on checking digital signatures
"Documents may be sent over public networks only if they are encrypted" - \rightarrow interceptor checks/implements encryption

Summary

ACLs and Capability Lists

Efficient data structures for implementing ACFs
Located in
OSs, middleware, DBMSe, application systems
Assume reliable authentication of subjects and objects
Are too weak to implement complex security policies

Interceptors

For implementing complex security policies
Located in
OSs, middleware (SELinux LSM-Framework and Security Server; CORBA Policy Objects)
Middleware platforms
- As "policy objects" (CORBA, Java)
- As part of a handler chain (Web services middleware, Apache Axis)

Cryptographic Security Mechanisms

Goal: Enforcement of access control policies

IBAC/ABAC/RBAC/MLS Models assume

Reliable identification of subjects and objects
Integrity and confidentiality of
Subjects and objects
Their communication (especially in distributed environments)

Encryption Techniques and Algorithms

Encryption

Transformation of a plaintext into a ciphertext
Decryption possible only if decrypt algorithm is known

Cryptosystem Components

2 functions encrypt, decrypt
2 keys k1, k2

Notation

text = decrypt_{k2}(encrypt_{k1}(text)) or simply
text = \{\{text\}_{k1}\}_{k2} (if encryption function is obvious)

Kerkhoff’s Principle

Encryption functions (algorithms) are publicly known

\rightarrow many experts look at it
\rightarrow quality advantage assumed

Keys are secret

\rightarrow encryption security depends on
- Properties of algorithms
- Confidentiality of keys

Classification of Encryption Schemes

Symmetric schemes (secret key): one single key: k1=k2
Asymmetric schemes (public key): two different keys: K1\not=K2

Symmetric Encryption Schemes

Characteristic Feature

Encryption and decryption with same key
\rightarrow security based on keeping key secret

Example

Key k\in\mathbb{N}
encrypt_k: shift letters of a plaintext backwards in alphabet by K positions
decrypt_k: shift letters of a ciphertext forward by K positions
ciphertext "HAL", key k=1 \rightarrow decrypt_1 ("HAL") = "IBM"

Application Examples

Confidentiality of Communication (Assumptions)

Sender and receiver share key k , which has to be established - Before communication - Authentically - Confidentially
Nobody else must know k(secretkey)

Authentication: E.g. client to server ( challenge-response - authentication by shared secret key)

Each client shares an individual andsecret key k_{client} with server
Server and clients keep key secret
Server reliably generates a nonce (= never sent once before )

Sealing of Documents, e.g. Capabilities

1 key owner \rightarrow owner may
- seal document
- check whether seal is sound
Group of key owners \rightarrow each group membermay
- Seal document
- Check whether seal was impressed by group member
- \rightarrow but also: nobody in this group can prove it was or wasn’t him (this is not yet a digital signature!)
Outside the group \rightarrow nobody can do any of these things

Algorithms

Block and Stream Ciphers
Block cipher
- Decompose plaintext into blocks of equal size (e.g. 64 bits)
- Encrypt each block
stream cipher
- Encrypt each digit of a plaintext stream by a cipher digit stream (e.g. by XOR)
- Cipher digit stream: pseudo-random digit stream

Block Cipher Algorithms

Data Encryption Standard (DES)
- Published 1975
- Keyspace 56 bits, 8 parity bits (much too small today)
- HW implementation ~10^9 bit/sec (FPGAs, 2002)
- No longer certified since 1998
Advanced Encryption Standard (AES)
- Standard since May 2002; variable key and block lengths
- Encryption speed (SmartCard 28MHz ARM processor) 2,4*10^6 bits/sec for 128 - bit keys, 128-bit block length

Asymmetric Encryption Schemes

Characteristic Feature: Encryption and decryption with different keys

\rightarrow key pair (k1,k2) = (k_{pub} , k_{sec}) where
decrypt_{k_{sec}} ( encrypt_{k_{pub}} (text)) = text
k_{pub}: public key
k_{sec}: private (secret) key

Conditio sine qua non

Secret key not computable from public key

Application Examples

Confidentiality of Communication (compare symmetric encryption schemes)
- Sender shares no secret with receiver
  - No key exchange before communication
  - Sender can be anonymous (e.g. electronic payment systems)
  - No trust between sender and receiver necessary
- Sender must know public key of receiver
- \rightarrow public-key-Infrastructures (PKIs) containing key certificates
Authentication: e.g. client to server
- challenge-response - authentication using public key
- Assumptions
  - Each client owns an individual key pair ( k_{pub}, k_{sec} )
  - Server knows public keys of clients (PKI)
  - Clients are not disclosing secret key
  - Server reliably generates nonces
  - Properties
    - Client and server share no secrets
      - No key exchange before communication
      - No mutual trust required
    - But: sender must know public key of receiver
    - \rightarrow PKIs
Sealing of Documents, e.g. Contracts (compare sealing using secret keys)
- \exists just 1 owner of secret key
- \rightarrow only she may seal contract
- Knowing her public key,
- \rightarrow everybody can check contract’s authenticity
- \rightarrow especially, everybody can prove that she was not the author
- \rightarrow repudiability; see below, digital signatures
- Consequence of Symmetric vs. Asymmetric Encryption
  - Symmetric: shared key, integrity and authenticity can be checked only by key holders \rightarrow message authentication codes (MACs)
  - Asymmetric: integrity and authenticity can be checked by anyone holding public key (because only holder of secret key could have encrypted the checksum \rightarrow digital signatures
Key Distribution for Symmetric Schemes
- Asymmetric encryption is expensive
  - Runtime > 3 orders of magnitude
  - Key pairs generation
    - High computational costs
    - High degree of trust needed in generating organization
  - Public Key Infrastructures needed for publishing public keys
    - Worldwide data bases with key certificates, certifying (public key \Leftrightarrow person)
    - Certification authorities
  - \rightarrow Use asymmetric key for establishing communication
    - Mutual authentication
    - Symmetric key exchange
  - Use symmetric encryption for communication

The RSA Cryptosystem ( Rivest / Shamir / Adleman , 1978)

Applied in >95% of today’s applications of asymmetric cryptosystems

Attractive because encrypt=decrypt

decrypt_{k_{sec}}(encrypt_{k_{pub}}(Text))
decrypt_{k_{pub}}(encrypt_{k_{sec}}(Text))

\rightarrow universal:

Confidentiality
Integrity and authenticity (non repudiability, digital signatures)

Problem: Inference of private key from public key

Idea: Link this problem to known hard problem: prime factorization \Leftrightarrow For n\in\mathbb{N} we search 2 primes p and q such that n=p*q

\rightarrow (believed to be) a hard problem because for factorization, prime numbers are needed
There are many of them, e.g. between 2^{512} and 2^{513} approx. 7*10^{151}
Finding them is extremely expensive: Sieve of Eratosthenes
- Memory O(n) \rightarrow 12 - digit primes ~4 Terabyte
- 64 digits: more memory cells than atoms in Solar system
- Current standard \geq 308 digits
Optimization: Atkin’s Sieve, O(n^{1/2+O(1)})

However ...

Until today, we only believe that for computing k_{sec} we need to factorize n; there might be a completely different way but: if we can compute k_{sec} in this way, we would have solved the factorization problem
Until today, no polynomial factorization algorithm is known
Until today, nobody proved that such algorithm cannot exist...

Precautions in PKIs: Prepare for fast exchange of cryptosystem (e.g. based on computation of logarithm in elliptic curves)

Attack on confidentiality

Ann with (k_{pub}, k_{sec}) is client of 2 servers Good und Bad: Bad listenswhile Good $\rightarrow$Ann: {X} kpub (someconfidentialmessage)

Next authenticationchallengeby Bad : Bad $\rightarrow$Ann: {X} kpub (uses{X} kpub as nonce ) Ann \rightarrow Bad : {{X} kpub } ksec = X (responsebyAnn)

Cause

nonce property violated
Samekey used for 2 differentpurposes (authentication, confidentiality) \rightarrow flawed use of security mechanism

Cryptographic Hash Functions

Goal

Discover violation of integrity of data
So that integrity of information is maintained

Method

Checksum generation by cryptographic hash functions
Checksum encryption
Integrity check by
- Generating a new checksum
- Decryption of encrypted checksum
- Comparison of both values

Method of Operation: Map data

Of arbitrary length
To checksum of fixed length such that Text1 \not= Text2 \Rightarrow hash(Text1) \not= hash(Text2) with high probability

Weak and Strong Hash Functions: One-way

\forall x\in X, hash:X\rightarrow Y is efficiently computable
There is no efficient algorithm that computes x from hash(x) \rightarrow given hash(x), it is practically impossible to compute an x\not= x' where hash(x‘)=hash(x)

Strong Hash Functions: + Collision-free

hash: X\rightarrow Y is a weak hash function
It is practically impossible to find x\not= x‘ where hash(x)=hash(x‘) (however, they do exist ...)

Algorithms: best before ...

160 - Bit checksums
- RIPEMD-160
  - For creating qualified digital signatures certified in Germany until end of 2010
  - For checking until end of 2015
- Secure Hash Algorithm (SHA-1, published NIST 1993)
  - 2010/2015
Larger Checksums
- SHA-256, SHA-384, SHA-512
No longer approved
- Message Digest (MD4 (1991), MD5 (1992)); 128-Bit checksum
  - MD5: belongs to IPsec algorithm group, used also in SSL
  - Since 1996 doubts concerning collisions

Digital Signatures

Goal

To assert author of a document (signer) \rightarrow Authenticity
To discover modifications after signing \rightarrow Integrity
\Rightarrow non-repudiability

Approach

Create signature
- Integrity: create checksum \rightarrow cryptographic hash function
- Authenticity: encrypt checksum \rightarrow use private key of signer
check signature
- Decryptchecksum using public key of signer
- Compare result with newly created checksum

Applications

Authenticity of documents
Orders to banks(HBCI / FinTS)
Contracts (signed by all parties)
Emails (e.g. from your bank)
Ownership proofs (cadastral register, permissions (capabilities))
Tax declarations
Key certificates
Authenticity of software
Downloads, OS updates, Microsoft device driver signatures

Cryptographic Attacks

4 Classes

Ciphertext Only attacks
Known Plaintext attacks
Chosen Plaintext attacks
Chosen Ciphertext attacks

Notation

T: plaintext
CT: ciphertext
Ke: encryption key, so CT =\{T\}_{Ke}
Kd: decryption key, so T=\{CT\}_{Kd}

Ciphertext Only Attacks (weakest assumptions)

Known: ciphertext CT
Wanted: plaintext T, Ke, Kd, algorithm
Typical assumptions
CT was completely generated by one Ke
Known algorithm

Examples

Observation of packet sequences in networks
Listening into password-based authentication with encrypted passwords

Known Plaintext Attacks

Known: T and CT (respectively parts thereof)
Wanted: Ke, Kd, algorithm

Example

Listening into challenge/response protocols
- Server \rightarrow Client: nonce
- Client \rightarrow Server: \{nonce\}_{Ke}
(countermeasure often: Client \rightarrow Server:${nonce + Time}_{Ke}$)

Chosen Plaintext Attacks

Known: T and CT where T can be chosen by attacker, CT observable
- attacker \rightarrow X:T
- X\rightarrow attacker:CT(=\{T\}_{Ke})
Wanted: Ke, Kd (algorithm often known)

Examples

Authentication in challenge/response protocols
Attacker (malicious server) tries to find client’s private key
sends tailored nonces
Authentication by chosen passwords
Attacker tries to find login password
Generates passwords and compares their encryptions with password data base

Chosen Ciphertext Attacks

Known: T,CT and Kd where CT can be chosen and T can be computed from CT
wanted: Ke
\rightarrow successful attacks allow forging digital signatures

Examples: Attack by

(within limits) Servers while authenticating clients
(within limits) Observers of such authentications
In a PK cryptosystem: Everybody knowing Kd (the whole world)

Scenario

Public network, public key encryption, RSA
Authentication and confidentiality using same key pairs
Client/server
One server is a bad guy

Summary

Goals of Cryptographic Algorithms

To provide security properties such as
Integrity, confidentiality, non-repudiability
- Of communication
- Of resources such as files, documents, program code
Especially: implement assumptions made by security models, such as
Authenticity, integrity, confidentiality of
- Model entities (subjects, objects, roles, attributes)
- Model implementations

Cryptographic Mechanisms

Encryption algorithms
Symmetric
Asymmetric
Hash functions
Combinationsof these mechanisms
Messageauthentication codes
Digitalsignatures

Beware: Many Pitfalls!

Weaknesses of mathematical foundations \rightarrow unproved assumptions
Weaknesses of algorithms \rightarrow cryptographic attacks
Weaknesses of key generation \rightarrow e.g. weak prime numbers
Weaknesses of mechanism use \rightarrow co-existence of mechanisms

Identification and Authentication

Goal: To reliably identify people, servers, systems, subjects, objects ... \rightarrow general term in authentication: "principal"

Required e.g. by

IBACpolicies
RBAC policies
User-to-role association
ABAC policies
Assignment of attributes to subjects and objects
MLS policies
Assignment of classes to subjects and objects

Approaches: Proof of identity by

By proving knowledge of simple secret \rightarrow passwords
By biophysicproperties \rightarrow biometrics
By proving knowledge of simple secret \rightarrow cryptographic protocols

Passwords

Used For: Authentication of humans to IT systems

Verified Item: Knowledge of simple secret

Properties

Convenient
Easy to guess / compute (RainbowCrack: 104*10^9 hash values per second \rightarrow 10 - digits passwords in 1,5 months ...)
- \rightarrow password generators ("TomApple")
- \rightarrow password checkers (min. 8 chars, no word from dictionary, not the dog, ...)
Easy to compute \rightarrow longpasswords
Problem of careless handling (password on post-it pinned to screen)
Input can easily be observed (see EC PINs)
Trust in system necessary, secret is exposed (EC-PINs)
Fundamental requirement in distributed systems:
- Confidential communication with authenticating system

Storing the Secret at 2 parties

Principal
Bio-mem, key store, memo
plaintext
Authentication service
Local data base, file ("/etc/passwd", "/etc/shadow")
Distributedsystems: centralized directory (LDAP server)
Encrypted byone-wayfunction
Password-DB (principal, hash (password))

Verifying a Secret (Principalname, hash(password)) = Password-DB.Principalname.hashvalue

Biometrics

Used For: Authentication of humans to IT systems

Verified Items: Individual properties such as voice, hand/retina geometry, finger print, Signature

Verification: By comparing probe with reference pattern

Pros
- Convenient, no secrets to remember, cannot be lost
- Difficult to intentionally pass on
- (prospectively) Difficult to counterfeit
Contras: Fundamental technical problems
- Comparison methods with reference fuzzy techniques, e.g. for finger prints: coordinate grid, geometry of minutiae
- FalseNon-match Rate (FNMR): authorized people are rejected
- False Match Rate (FMR): not authorized people are accepted

Some numbers (2008) :

Finger print readers: FMR 0,01% -0,0001% (1 von 10^4 - 106 ), FNMR 1%
Iris readers: FMR \sim 0,0008%

More Technical Issues

Susceptible wrt. environmental conditions
Voice: noise
Finger print: dirt
Signature: nervousness, fractured arm
Trust in system required
Fundamental weaknesses in distributed systems
Secure communication to authenticating system required (personal data)

Organizational Costs

Reference probes are personal data \rightarrow Data Protection Act
Reaction time on security incidents
Passwords, smartcards can be exchanged easily
Fingers or eyes ...

Social Barriers

Not easily accepted
Finger prints: criminal image
Retina
Some weekend entertainments identifiable
Some diseases identifiable
Naive advertising calls for distrust
Politically: "Biometrician undesired on national security congress"
Technically: for many years unkeptpromise to cure weaknesses

Cryptographic Protocols

SmartCards

Used For: Authentication of humans to IT systems

Verified Item: Knowledge of complex secret

Secret part of asymmetric key pair
Symmetric key

Verification

Challenge/response protocols
Goal
- Proof that secret is knows
- Contrary to password authentication, no secret exposure

Vehicle for Humans: SmartCards

Small Computing Devices Encompassing
- Processor(s)
- RAM
- Persistent memory
- Communication interfaces
What They Do
- Store and keep complex secrets (keys)
- Run cryptographic algorithms
- Response to challenges in challenge/response protocols
- Encryptincoming nonces
- Launch challenges to authenticate other principals
- Generate nonces, verify response

Usage... e.g. via plug-ins in browsers

Properties

no secret is exposed
- \rightarrow no trust in authenticating system required
- \rightarrow no trust in network required
Besides authentication other features possible
- \rightarrow digital signatures, credit card, parking card ...
Weak verification of card right to use card (PIN, password)
- \rightarrow some cards have finger print readers
Power supply for contactless cards

Authentication Protocols

Used For: Authentication between IT systems

Method: challenge/response-scheme

Based on

symmetric key: principal and authenticating system share secret
asymmetric key: authenticating system knows public key of principal

Authentication Using Secret Keys The Fundamentals: 2 Scenarios

After one single authentication, Alice wants to use all servers in a distributed system of an organization. "Here is a nonce I encrypted using Alice‘s secret key. Prove that you are Alice by decrypting it."
Alice wants authentic and confidential communication with Bob. Authentication Server serves session keys to Bob and Alice

Needham-Schroeder Authentication Protocol

for secret keys
Goal: To establish authentic and confidential communication between 2 Principals
Method
1. Authentication of Alice to Bob \rightarrow Bob knows the other end is Alice
2. Authentication of Bob to Alice \rightarrow Alice knows the other end is Bob
3. Establish a fresh secret between Alice and Bob: a shared symmetric session key \rightarrow confidentiality, integrity, authenticity

Fundamental

Common trust in same authentication server
Client-specific secret keys (K_{AS}, K_{BS})

Note: Protocol used in Kerberos security architecture

Message Semantics

A\rightarrow S:A,B,N_A: A requests session key for B from S
S\rightarrow A:\{N_A,B,K_{AB},K_{AB},A\}_{KBS}\}_{KAS}: S responds encrypted with K_{AS} such that only A is able to understand
- nonce proves that 2. is a reply to 1. (fresh)
- session key K_{AB}
- ticket for B; encryption proves that K_{AB} was generated by S (S assures that K_{AB} belongs to A)
A\rightarrow B:\{K_{AB},A\}_{KBS}: A gives ticket to B; encryption serves as challenge
B\rightarrow A:\{N_B\}_{KAB}: B decrypts ticket (response) and verifies whether A knows K_{AB} (challenge)
A\rightarrow B:\{N_B-1\}_{KAB}: A proves by using K_{AB} that he was the sender of 3. (response)

Authentication of A to B: only A can decrypt 2.; freshness from 5.
Authentication of B to A: only B can decrypt 3. (use of K_{AB} in 4. must be recognizable by A (recognizable nonce, e.g. timestamp))
A and B now also share a secret session key

Authentication Servers

Common trust in server by all principals
\rightarrow closed user group, in general belonging to same organization
Servershares individual secret with each principal (symmetric key)

Needham-Schroeder Authentication Protocol for public keys

Goal: To establish authentic and confidential communication between Principals
Method
1. Authentication of Alice to Bob \rightarrow Bob knows the other end is Alice
2. Authentication of Bob to Alice \rightarrow Alice knows the other end is Bob
3. Establish a fresh secret between Alice and Bob: a shared symmetric session key \rightarrow confidentiality, integrity, authenticity
Premise: Trust
- Individually in issuer of certificate (certification authority, CA)
- Not necessary: in certificate server!
- \rightarrow much weaker than secret key based authentication
Message Semantics
1. A\rightarrow S:A,B: A requests public key of B
2. S\rightarrow A:\{PK_B,B\}_{SK_S}: S sends certificate; A must know public key of CA
3. $A\rightarrow B:{N_A,A}_{PK_B}: A sends challenge to B
4. B\rightarrow S:B,A: B requests public key of A
5. S\rightarrow B:\{PK_A,A\}_{SK_S}: S responds (see 2.)
6. B\rightarrow A:\{N_A, N_B\}_{PK_A}: B proves it is B (response) and challenges A with N_B
7. $A\rightarrow B:{N_B}_{PK_B}: A replies to challenge and proves that it is A
- Authentication of A to B: 6. together with 7. (only A can decrypt 6.)
- Authentication of B to A: 3. together with 6. (only B can decrypt 3.)
- From WHERE the key certificates actually are obtained is irrelevant

Certificate Servers: Basis of Authentication

Key certificates
Digitally signed mappings (name \leftrightarrow public key)
Issued by certification authorities (CAs, trustworthy organizations)
Certificate servers
Manage certificate data base
Need not be trustworthy

\delta s Between Secret Key and Public Key Authentication

Functionality
- SK:
  - Requires common trust in AS, á-priori key exchange, and mutual trust in keeping session key secret
  - Allows for message authentication codes
- PK:
  - Requires knowledge of public keys \rightarrow PKIs
  - Allows for digital signatures
Availability
- SK: Require online AS
- PK: Allow for local chaching of certificates (e.g. in web browsers)
Key Management
- security
  - SK: accumulation of secrets at AS \rightarrow dangerous, server always online
- complexity
  - SK:
    - n keys for authenticating n principals
    - O(n^2) session keys for n communicating parties
  - PK:
    - n keys for authenticating n principals
    - O(n) keys for n communicating parties if PKs are used
    - O(n^2) key for n communicating parties if session keys are used
    - Certificate management: PKIs, CAs, data bases, callbacks list, ...

Summary

TCB: All functions of an IT system necessary and sufficient to establish its security properties

Basic Security Mechanisms of a TCB

Authentication
- \rightarrow passwords, biometrics, symmetric and asymmetric encryption, challenge/response protocols, hash functions, message authentication codes, digital signatures
Authorization
- \rightarrow ACLs, capability lists, interceptors, policy servers/objects
- \rightarrow symmetric and asymmetric cryptosystems
\rightarrow confidentiality, integrity, ...
- Of subjects and objects
- Of their communication
Problems Only Touched
- Multitude and diversity of security mechanisms
- Distributed among
  - OS
  - Middleware platforms
  - Libraries
  - Application software
- \rightarrow large, distributed TCB with fuzzy perimeter
- \rightarrow set of all security mechanisms \not = security architecture

Security Architectures

Trusted Computing Base (TCB) The set of functionsof an IT system that are necessary and sufficient for implementing its security properties \rightarrow Isolation, Policy Enforcement, Authentication ...

Security Architecture The part(s) of a system’s architecture that implement its TCB \rightarrow Security policies, Security Server (PDP) and PEPs, authentication components, ...

Security Mechanisms Algorithms and data structures for implementing functions of a TCB \rightarrow Isolation mechanisms, communication mechanisms, authentication mechanisms, ...

\rightarrow TCB \sim runtime environment for security policies

Security architectures have been around for a long time ...

Architecture Components
- Buildings, walls, windows, doors, moats, draw bridges
Architecture
- Component arrangement and interaction
Goal of a Security Architecture: Build a stronghold such that security policies can be enforced
- Presence of necessary components/mechanisms
- Totality of interaction control ("mediation")
- Tamperproofness
- \rightarrow architecture design principles

Check your trust in

Completeness of access mediation (and its verification!)
Policy tamperproofness(and its verification!)
TCB correctness (and its verification!)

Problem Areas

PDPs (policy decision points)
PEPs (policy enforcement points)

are

Scattered among many OS components \rightarrow Problem of security architecture design
Not robust
- Notisolated from errors within the entire OS implementation
- Especially in dynamically loaded OS modules
- \rightarrow Problem of security architecture implementation

... and that is not all

Linux set-user-id programs

Execute security-sensitive operations
Adopt different effective user id during execution
Thushave different privileges
Especially those of user id "root"
\rightarrow each such program is part of the TCB!
e.g.
- /usr/bin/passwd (> 50 y old open sourceprogram)
- /opt/google/chrome/chrome-sandbox (author: Google!)
- /usr/lib/virtualbox/VirtualBox (author: Oracle!)

Problem

OSes/Middleware/Applications are big
Only a small set of their functions logically belongs to the TCB
\rightarrow Security architecture design such that TCB functions are collected in an
- not bypassable (total access mediation),
- isolated (tamperproofness),
- trustworthy (verifiable correctness) core
- \rightarrow Security architecture implementation such that these properties are enforced

Architecture Design Principles

Goal

Complete
Tamperproof
Verifiably correct
control of all security-relevant actions in a system

Approach: Definitions of fundamental security architecture design principles

The Reference Monitor Principles

There Exists an Architecture Component that is

(RM 1) Involved in any subject/object interaction
- \rightarrow total mediation property
(RM 2) Well-isolated from the rest of the system
- \rightarrow tamperproofness
(RM 3) Small and well-structured enough to analyze correctness by formal methods
- \rightarrow verifiability

A security architecture component built along these principles: "Reference Monitor"

Idea

1 PDP (policy implementation)
many PEPs (interceptors, policy enforcement)

Reference Monitor

Core component of a TCB
Typically encloses
- Security policy implementation(s) (PDP)
  - Model state (e.g. ACM, subject set, entity attributes)
  - Model behavioral logic (e.g.authorization scheme)
- Enforcement mechanisms: PEPs
Typically excludes (due to complexity and size, RM 3)
- Authentication
- Cryptographic mechanisms
- Sometimes also model state (e.g.ACLs)

Consequences of (RM 3) for TCBs

Few functions \rightarrow small size (LoC)
Simple functions \rightarrow low complexity
Strong isolation
Precisely known perimeter

Implementation Layers

Policy-controlled OS - Monolithic OS Kernel
- OS level policy:
- TCB - Functional View:
- TCB - Implementation View:
Policy-controlled OS - Microkernel Architecture ( Nizza )
- OS level policy:
- TCP functional view:
- TCP implementation view:
Policy-controlled Application: Middleware-level Policy
- Example: Web Services on Apache Axis-2 Webservers
Policy-controlled Application
- Application-level Policy
- TCB - Functional View
- TCB - Implementation View

State of the Art

Numerous rather weak implementations in
- Commodity OSes
- Middleware
- Applications
Stronger approaches in
- Microkernel OSes (e.g.L4/Nizza)
- Security-focused OSes(e.g.SELinux, Trusted BSD, Open Solaris)

Security Architectures of Operating Systems

Goal: OS security architecture such that reference monitor principles

(RM 1) Involved in any subject/object interaction
(RM 2) Well isolated from the rest of the system
(RM 3) Small and well-structured

for security policy are satisfied

Nizza

Goals

RM 1 - RM 3
- Especially: Small TCB
Maintain functionality of
- Contemporary legacy OSes
- Legacy Applications ("legacy" = unmodified for security)

Concepts

Reference monitor principles: Separation of OS, Applications into security-critical vs. non-critical components
- \rightarrow precise identification of a (minimal) TCB
Maintain functionality
- \rightarrow Paravirtualization of standard, legacy OSes

OS View

Trustworthymicrokernel
Trustworthybasic services
Nottrustworthy (paravirtualized) legacy OS

Application View

Vulnerability increaseswith growing complexity
- \rightarrow reduce vulnerability of security-critical code by
Software functionality separation
Isolation of functional domains
Example: Email Client
- Non-critical: reading/composing/sending emails
- Critical: signing emails (email-client \leftrightarrow Enigmail Signer)

Putting it all Together

Size of TCB: 105 000 LoC

Microkernel (L4/Fiasco): 15 000 LoC
Trustworthy basic services: 35 000 LoC
Enigmail Signer: 55 000 LoC

Results

Code size of TCB reduced by 2 orders of magnitude (100 000 LOC vs. 10 000 000 LOC!)
Functionality of legacy OSes and applications preserved

Costs

(Moderate) performance penalties
Paravirtualization of legacy OS
Decomposition of trusted applications

Security Enhanced Linux (SELinux)

Goal: Security-aware OS

State-of-the-art OS
State-of-the-art security paradigms

Idea: Policy-controlled (Linux) OS kernel

Approach SELinux =

Linux (DAC, IBAC)
MAC, RBAC, ABAC, TAM, MLS

Security Policies in SELinux

Implementation by new OS abstractions (unheard of for > 40 years...)
Somewhat comparable to " process" abstraction!
- Specification ...
  - of a process is a program: algorithm implemented in formal language (C++, Rust, Python, Java, ...)
  - of a security policy is a security model: rule set in formal language (predicate logic, automaton theory, DynaMo, ...)
- Runtime environment (RTE) ...
  - of a process is OSprocess management \rightarrow RTE for application-level programs
  - of a security policy is OS security Server \rightarrow RTE for kernel-level policies

SELinux Architecture

Policy-aware Security Server (policy decision point, PDP)
- Policy RTE in kernel‘s protectiondomain
Interceptors (policy enforcement points, PEPs)
- Total interaction control in object managers (part of Linux Security Module Framework)

Implementation Concepts

Reference Monitor Principles
- Total mediation of security-relevant interactions
  - \rightarrow placement of PEPs: Integration into object managers
- Tamperproofness of policy implementation
  - \rightarrow placement of PDP: Integration into kernel ( security server )
Policy Support
- Remember: Security policies require
- Authenticity of entities: Unique subject/object identifiers
- Policy-specific entity attributes (type, role, MLS label)
Problem in Linux,
- Subject identifiers (PIDs) or object identifiers (i-node numbers)are
  - neither unique
  - nor are of uniform type
- \rightarrow security identifier (SID)
- Policy-specific subject/object attributes (type, role, MLS label) are
  - not part of subject/object metadata (task struct, i-node struct)
  - \rightarrow security context
- \rightarrow Approach: Extensions of process/file/socket...-management

Authenticity of Entities

Object managers help: implement injective mapping SÈO \rightarrow SID
- SID created by security server
- Mapping of SIDs to objects by object managers

Entity Attributes

Security policy implements injective mapping SID \rightarrow security context
security contexts creation according to policy-specific labeling rules
Entry in SID \rightarrow security context mapping table

Security Context contains

Standard entity attributes such as
- Unique user ID
- Role
- Type (user_t , tomCat_t, ...)
- Class (process, file, ...)
Policy-specific entity attributes such as
- Confidentiality/clearance level (e.g.MLS label)

is implemented as

A text string with policy-dependent format:

$ ps -Z
bob:doctor_r:shell_t:s0-s0:c0.c255 4056 pts/2 00:00:00 bash

Problem: Security contexts of persistent Entities

Policies not aware of persistency of entities, e.g. files (that continue existing over system shutdowns)
- \rightarrow persistency of security contexts is job of object managers
Layout of object metadata (e.g. inodes in a file system) is file system standard
- \rightarrow security contexts cannot be integrated in i-nodes (their implementation: policy-independent)!

Solution

Persistent objects additionally have (OM-local) persistent SID : "PSID"
OMs map these to SID
3 invisible storage areas (\sim files) in persistent memory(HDD, SSD) implementing
- Security context of file system itself (label)
- Bijective mapping: inode \rightarrow PSID
- Bijective mapping: PSID \rightarrow security context

Access Vector Cache(AVC)

Located in object managers (user level) resp. in Security Server (kernel level)
Caches access decisions

Summary SELinux

Motivation
- Out-of-date and weak security mechanisms in contemporary commodity OSs
Consequence
- Insufficient and inadequate security specification paradigms
- Numerous vulnerabilities
Approach
- New OS abstraction: Security policies
- \rightarrow policy-controlled OS: DAC & MAC, IBAC, RBAC, ABAC, TAM, MLS

RM Evaluation of SELinux

Compliance with Reference Monitor Principles
1. Total Mediation Property (placement of PEPs) (currently) done manually
2. Tamperproofness of Policy Implementation
  - Fundamental problem in monolithic software architectures
  - \rightarrow TCB implementation vulnerable from entire OS kernel code
  - Security server
  - All object managers
  - Memory management
  - IPC implementation
  - I/O system
  - It can be done: Nizza
3. Verifiability
  - Size and complexity of policy (reference policy \sim 50.000 rules) \rightarrow analysis tools
  - Policy‘s RTE claim to be universal
  - Completeness of PEPs
  - Policy isolation

Security Architectures of Distributed Systems

Scenario:

CORBA

Integration of Security Policies

Web Services

Integration of Security Policies

Example: Apache Axis-2 Webserver

Kerberos

Authentication and authorization architecture for distributed systems with closed user groups( \rightarrow static sets of subjects)

Target Scenario:

Distributed system run by single organization
Workstations and Servers
2 Kerberos servers
- Authentication Server (AS)
- Authorization Server (TGS)

History:

MIT Athena project, started 1986
Concepts are part of CORBA‘s Basic CORBA Security Architecture specification
Originally designed for: 650 workstations, 65 servers, 5000 users (1988)

Principal Architecture Components

Authentication Server (AS)
- Authenticates users
  - Based on (symmetric) key shared between user and AS
  - Result: electronic ID card (authenticator)
- Authorizes use of TGS
  - Based on key shared between AS and TGS
  - Result: ticket (capability) for TGS
Ticket Granting Server (TGS)
- Issues tickets for all servers
  - Based on key shared between TGS and respective server
  - Result: ticket(s) for server(s)
Kerberos database
- Contains for each user and server a mapping \langleuser, server\rangle\rightarrow authentication key
- Used by AS
- Is multiply replicated (availability, scalability)

Typical Use Case

Authentication, then request for TGS ticket
Authenticator, TGS-Ticket
Request for further server tickets
Server tickets
Service request: Servers decide based on

authenticator of client
server ticket
(tentatively) local security policies

Kerberos Cryptography: Inside Kerberos Tickets

Tickets

Issued by Ticket Granting Server
Specify right of one client to use one server (personalized capability)
Limited lifetime (to make cryptographic attacks difficult)
- \sim 1 day; balance between secure and convenient
- Short: inconvenient but more secure (if ticket is stolen it soon expires)
- Long: insecure but more convenient (no need for frequent renewal)
Can be used multiply while valid
Are sealed by TGS with key of server

T_{Client/Server}=\{Client, Server, Client.NetworkAddress, Timestamp, Lifetime, SessionKey_{Client/Server}\}_{KTGS/Server}

Provisions against Misuse

Tampering by client to fabricate rights for different server
- \rightarrow provision: guarantee of integrity by MAC using KTGS/Server
Use by third party intercepting ticket
- \rightarrow provision: personalization by
- Name and network address of client together with
  - Limited lifetime
  - Authenticator of client \rightarrow

Authenticators

Proof of identity of client to server
Created using SessionKeyClient/Server
- \rightarrow can be created and checked only by
- Client (without help by AS, because client knows session key (see below))
- Server
- TGS (trusted)
Can be used exactly once
- \rightarrow prevent replay attacks by checking freshness

A_{Client}=\{Client, Client.NetworkAddress, Timestamp\}_{SessionKey_{Client/Server}}

The Complete Process

Single Steps:

Alice tells her name
Alice’s workstation requests authentication
The AS

Creates fresh timestamp
Creates a fresh session key for Alice's communication with the TGS: SessionKey_{Alice/TGS}
Creates Alice’s ticket for TGS and encrypts it with K_{AS/TGS} (so Alice cannot modify it): Ticket_{Alice/TGS}=\{Alice, TGS, ..., SessionKey_{Alice/TGS}\}_{K_{AS/TGS}}
Encrypts everything with K_{Alice/AS} (so only Alice can read the session key and the TGS-Ticket) \{TGS, Timestamp , SessionKey_{Alice/TGS}, Ticket_{Alice/TGS}\}_{K_{Alice/AS}}

Alice’s workstation

Now has {TGS, Timestamp , SessionKey_{Alice/TGS} , Ticket_{Alice/TGS}\}_{K_{Alice/AS}}
Requests Alice’s password
Computes K_{Alice/AS} from password using a cryptographic hash function
Uses it to decrypt above message from AS
Result: Alice’s workstation has
- Session key for TGS session: SessionKey_{Alice/TGS}
- Ticket for TGS: Ticket_{Alice/TGS}
- The means to create an authenticator

Using a Server

Authentication (bidirectional)

2 Steps

Authentication of client to server
Authentication of server to client (optional)
Authentication of Client

Assumptions
- Alice has session key
- Alice has server ticket

Alice assembles authenticator A_{Alice}=\{Alice,Alice’s network address,timestamp\}_{SessionKey_{Alice/Server}} Only Alice can do that, because only she knows SessionKey_{Alice/Server}
Alice sends Ticket_{Alice/Server}, A_{Alice} to Server
Server decrypts ticket and thus gets session key; thus it can decrypt A_{Alice} and check:
- Freshness
- Compliance of names in ticket and authenticator
- Origin of message (as told by network interface) and network address in authenticator
Authentication of Servers

Server sends \{Timestamp+1\}_{SessionKey_{Alice/Server}} to Alice
Can only be done by principal that knows SessionKey_{Alice/Server}
This can only be a server that can extract the session key from the ticket Ticket_{Alice/Server}=\{Alice,Server ,..., SessionKey_{Alice/Server}\}_{K_{TGS/Server}}

Getting a Ticket for a Server

Are valid for a pair \langleclient, server\rangle
Are issued (but for TGS-Ticket itself) only by TGS
Ticket request to TGS: (server, TGS ticket, authenticator)

TGS:

Checks Ticket_{Alice/TGS} and authenticator
Generates session key for client and server: SessionKey_{Alice/Server}
Generates ticket: Ticket_{Alice/Server}
Encrypts both using shared session key \{Server, SessionKey_{Alice/Server}, Ticket_{Alice/Server}\}_{SessionKey_{Alice/TGS}}

Kerberos Summary

Distributed Authentication and Authorization Architecture

2 Security servers
- Authentication
- Authorization
Cryptographic mechanisms for
- Authentication
- Symmetric encryption: Confidentiality + Integrity + Authenticity
  - of communication
  - of tickets and authenticators
Kerberos TCB: TCBs of all OSes+
- Authentication Server
- Ticket GrantingServer
- Kerberos database
- All PDPs and PEPs on the servers
- Time server (\rightarrow signed time stamps!)

Summary

Current TCBs

Huge
Inhomogeneous
Distributed
Hard to identify precisely

Security Architectures’ Problem

Compliance to reference monitor principles

Case Studies

Nizza
SELinux
CORBA, Web Services, Kerberos
\rightarrow Huge challenges!

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Introduction

Risk Scenarios

Security Goals

Message

Security Engineering

Lecture Roadmap

Security Requirements

Motivation

Vulnerability Analysis

Human Vulnerabilities

Indirect Information Flow in Access Control Systems

Organizational Vulnerabilities

Technical Vulnerabilities

Buffer Overflow Attacks

Necessary Knowledge and Skills

Preparing the Attack

Result:

Self-Study Task

Summary

Threat Analysis

Attack Objectives and Attackers

Attack Methods

Examples

Root Kits

Damage Potential

Summary

Risk Analysis

Examples

Risk Classification

Risk Matrix

General Fact: Damage potential is highly scenario-specific

Occurrence Probability Assessment

Advisory Board Output Example

Security Policies and Models

Security Policies

Terminology

Example 1: Excerpt from the Unix Security Policy

Example 2: Excerpt from the AlphaCompany Security Policy

Implementation Alternative A

Implementation Alternative B

Security Models

Access Control Models

DAC vs. MAC

Identity-based Access Control Models (IBAC)

Access Control Matrix

The Harrison-Ruzzo-Ullman Model (HRU)

Deterministic Automata

HRU Security Model

State Transition Scheme (STS)

HRU Model Analysis

HRU Safety

Proof of Theorem

(A) Restricted Model Variants

(B) Heuristic Analysis Methods

Summary HRU Models

The Typed-Access-Matrix Model (TAM)

TAM Safety Decidability

Acyclic TAM Models

Roles-based Access Control Models (RBAC)

RBAC Access Control Function

RBAC96 Model Family

RBAC Summary

Attribute-based Access Control Models

ABAC Access Control Function

ABAC Security Model

ABAC Access Control Function

ABAC Summary

Information Flow Models

The Denning Model

Multilevel Security (MLS)

The Bell-LaPadula Model

Lattice vs. ACM

Consistency of L,cl, and m

BLP Security

BLP Model Summary

The Biba Model

Non-interference Models

Example 1: Multi-application Smart Cards

Example 2: Server System

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