vulnerabilities and threats in distributed systems * prof. bharat bhargava dr. leszek lilien...
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Vulnerabilities and Threatsin Distributed Systems*
Prof. Bharat BhargavaDr. Leszek Lilien
Department of Computer Sciences and the Center for Education and Research in Information Assurance and Security
(CERIAS )Purdue University
www.cs.purdue.edu/people/{bb, llilien}
Presented by
Prof. Sanjay Madria
Department of Computer ScienceUniversity of Missouri-Rolla
* Supported in part by NSF grants IIS-0209059 and IIS-0242840
ICDCIT 2004 2
Prof. Bhargava thanks the organizers of the 1st International Conference on Distributed Computing & Internet Technology—ICDCIT 2004. In particular, he thanks:
Prof. R. K. ShyamsunderProf. Hrushikesha MohantyProf. R.K. GhoshProf. Vijay KumarProf. Sanjay Madria
He thanks the attendees, and regrets that he could not be present.
He came to Bhubaneswar in 2001 and enjoyed it tremendously. He was looking forward to coming again.
He will be willing to communicate about this research. Potential exists for research collaboration. Please send mail to [email protected]
He will very much welcome your visit to Purdue University.
ICDCIT 2004 3
From Vulnerabilities to Losses Growing business losses due to vulnerabilities in
distributed systems Identity theft in 2003 – expected loss of $220 bln worldwide ;
300%(!) annual growth rate [csoonline.com, 5/23/03] Computer virus attacks in 2003 – estimated loss of $55 bln
worldwide [news.zdnet.com, 1/16/04]
Vulnerabilities occur in: Hardware / Networks / Operating Systems / DB systems /
Applications
Loss chain Dormant vulnerabilities enable threats against systems Potential threats can materialize as (actual) attacks Successful attacks result in security breaches Security breaches cause losses
ICDCIT 2004 4
Vulnerabilities and Threats Vulnerabilities and threats start the loss chain
Best to deal with them first
Deal with vulnerabilities Gather in metabases and notification systems info on
vulnerabilities and security incidents, then disseminate it
Example vulnerability and incident metabases CVE (Mitre), ICAT (NIST), OSVDB (osvdb.com)
Example vulnerability notification systems CERT (SEI-CMU), Cassandra (CERIAS-Purdue)
Deal with threats Threat assessment procedures
Specialized risk analysis using e.g. vulnerability and incident info
Threat detection / threat avoidance / threat tolerance
ICDCIT 2004 5
Outline
1. Vulnerabilities
2. Threats
3. Mechanisms to Reduce Vulnerabilities and Threats3.1.Applying Reliability and Fault
Tolerance Principles to Security Research
3.2.Using Trust in Role-based Access Control
3.3.Privacy-preserving Data Dissemination 3.4.Fraud Countermeasure Mechanisms
ICDCIT 2004 6
Vulnerabilities - Topics
Models for Vulnerabilities Fraud Vulnerabilities Vulnerability Research Issues
ICDCIT 2004 7
Models for Vulnerabilities (1)
A vulnerability in security domain – like a fault in reliability domain A flaw or a weakness in system security procedures,
design, implementation, or internal controls Can be accidentally triggered or intentionally
exploited, causing security breaches
Modeling vulnerabilities Analyzing vulnerability features Classifying vulnerabilities Building vulnerability taxonomies Providing formalized models
System design should not let an adversary know vulnerabilities unknown to the system owner
ICDCIT 2004 8
Models for Vulnerabilities (2) Diverse models of vulnerabilities in the literature
In various environments Under varied assumptions Examples follow
Analysis of four common computer vulnerabilities [17] Identifies their characteristics, the policies violated by
their exploitation, and the steps needed for their eradication in future software releases
Vulnerability lifecycle model applied to three case studies [4] Shows how systems remains vulnerable long after
security fixes Vulnerability lifetime stages:
appears, discovered, disclosed, corrected, publicized, disappears
ICDCIT 2004 9
Models for Vulnerabilities (3)
Model-based analysis to identify configuration vulnerabilities [23]
Formal specification of desired security properties Abstract model of the system that captures its
security-related behaviors Verification techniques to check whether the
abstract model satisfies the security properties
Kinds of vulnerabilities [3]
Operational E.g. an unexpected broken linkage in a distributed
database Information-based
E.g. unauthorized access (secrecy/privacy), unauthorized modification (integrity), traffic analysis (inference problem), and Byzantine input
ICDCIT 2004 10
Models for Vulnerabilities (4)
Not all vulnerabilities can be removed, some shouldn’tBecause:
Vulnerabilities create only a potential for attacks Some vulnerabilities cause no harm over entire system’s life
cycle Some known vulnerabilities must be tolerated
Due to economic or technological limitations Removal of some vulnerabilities may reduce usability
E.g., removing vulnerabilities by adding passwords for each resource request lowers usability
Some vulnerabilities are a side effect of a legitimate system feature
E.g., the setuid UNIX command creates vulnerabilities [14]
Need threat assessment to decide which vulnerabilities to remove first
ICDCIT 2004 11
Fraud Vulnerabilities (1)
Fraud:a deception deliberately practiced in order to secure unfair or unlawful gain [2]
Examples: Using somebody else’s calling card number Unauthorized selling of customer lists to
telemarketers(example of an overlap of fraud with privacy breaches)
Fraud can make systems more vulnerable to subsequent fraud Need for protection mechanisms to avoid future
damage
ICDCIT 2004 12
Fraud Vulnerabilities (2)
Fraudsters: [13] Impersonators
illegitimate users who steal resources from victims(for instance by taking over their accounts)
Swindlers legitimate users who intentionally benefit from the system or other users by deception(for instance, by obtaining legitimate telecommunications accounts and using them without paying bills)
Fraud involves abuse of trust [12, 29] Fraudster strives to present himself as
a trustworthy individual and friend The more trust one places in others the more
vulnerable one becomes
ICDCIT 2004 13
Vulnerability Research Issues (1)
Analyze severity of a vulnerability and its potential impact on an application Qualitative impact analysis
Expressed as a low/medium/high degree of performance/availability degradation
Quantitative impact E.g., economic loss, measurable cascade effects, time to
recover
Provide procedures and methods for efficient extraction of characteristics and properties of known vulnerabilities Analogous to understanding how faults occur Tools searching for known vulnerabilities in
metabases can not anticipate attacker behavior Characteristics of high-risk vulnerabilities can be
learnt from the behavior of attackers, using honeypots, etc.
ICDCIT 2004 14
Vulnerability Research Issues (2)
Construct comprehensive taxonomies of vulnerabilities for different application areas Medical systems may have critical privacy
vulnerabilities Vulnerabilities in defense systems compromise
homeland security
Propose good taxonomies to facilitate both prevention and elimination of vulnerabilities
Enhance metabases of vulnerabilities/incidents Reveals characteristics for preventing not only
identical but also similar vulnerabilities Contributes to identification of related vulnerabilities,
including dangerous synergistic ones Good model for a set of synergistic vulnerabilities can lead
to uncovering gang attack threats or incidents
ICDCIT 2004 15
Vulnerability Research Issues (3)
Provide models for vulnerabilities and their contexts The challenge: how vulnerability in one context
propagates to another If Dr. Smith is a high-risk driver, is he a trustworthy doctor?
Different kinds of vulnerabilities emphasized in different contexts
Devise quantitative lifecycle vulnerability models for a given type of application or system Exploit unique characteristics of vulnerabilities &
application/system In each lifecycle phase:
- determine most dangerous and common types of vulnerabilities- use knowledge of such types of vulnerabilities to prevent them
Best defensive procedures adaptively selected from a predefined set
ICDCIT 2004 16
Vulnerability Research Issues (4)
The lifecycle models helps solving a few problems Avoiding system vulnerabilities most efficiently
By discovering & eliminating them at design and implementation stages
Evaluations/measurements of vulnerabilities at each lifecycle stage
In system components / subsystems / of the system as a whole
Assist in most efficient discovery of vulnerabilities before they are exploited by an attacker or a failure
Assist in most efficient elimination / masking of vulnerabilities(e.g. based on principles analogous to fault-tolerance)
OR: Keep an attacker unaware or uncertain of important system
parameters(e.g., by using non-deterministic or deceptive system behavior, increased
component diversity, or multiple lines of defense)
ICDCIT 2004 17
Vulnerability Research Issues (5)
Provide methods of assessing impact of vulnerabilities on security in applications & systems
Create formal descriptions of the impact of vulnerabilities Develop quantitative vulnerability impact evaluation methods Use resulting ranking for threat/risk analysis Identify the fundamental design principles and guidelines for
dealing with system vulnerabilities at each lifecycle stage Propose best practices for reducing vulnerabilities at all
lifecycle stages (based on the above principles and guidelines) Develop interactive or fully automatic tools and infrastructures
encouraging or enforcing use of these best practices Other issues:
Investigate vulnerabilities in security mechanisms themselves Investigate vulnerabilities due to non-malicious but threat-
enabling uses of information [21]
ICDCIT 2004 18
Outline
1. Vulnerabilities
2. Threats
3. Mechanisms to Reduce Vulnerabilities and Threats3.1.Applying Reliability and Fault
Tolerance Principles to Security Research
3.2.Using Trust in Role-based Access Control
3.3.Privacy-preserving Data Dissemination 3.4.Fraud Countermeasure Mechanisms
ICDCIT 2004 19
Threats - Topics
Models of Threats Dealing with Threats
Threat Avoidance Threat Tolerance Fraud Threat Detection for Threat
Tolerance Fraud Threats Threat Research Issues
ICDCIT 2004 20
Models of Threats
Threats in security domain – like errors in reliability domain
Entities that can intentionally exploit or inadvertently trigger specific system vulnerabilities to cause security breaches [16, 27]
Attacks or accidents materialize threats (changing them from potential to actual)
Attack - an intentional exploitation of vulnerabilities Accident - an inadvertent triggering of vulnerabilities
Threat classifications: [26] Based on actions, we have:
threats of illegal access, threats of destruction, threats of modification, and threats of emulation
Based on consequences, we have:threats of disclosure, threats of (illegal) execution,
threats ofmisrepresentation, and threats of repudiation
ICDCIT 2004 21
Dealing with Threats
Dealing with threats Avoid (prevent) threats in systems Detect threats Eliminate threats Tolerate threats
Deal with threats based on degree of risk acceptable to application
Avoid/eliminate threats to human life Tolerate threats to noncritical or redundant components
ICDCIT 2004 22
Dealing with Threats – Threat Avoidance (1)
Design of threat avoidance techniques - analogous to fault avoidance (in reliability)
Threat avoidance methods are frozen after system deployment Effective only against less sophisticated attacks
Sophisticated attacks require adaptive schemes for threat tolerance [20]
Attackers have motivation, resources, and the whole system lifetime to discover its vulnerabilities
Can discover holes in threat avoidance methods
ICDCIT 2004 23
Dealing with Threats – Threat Avoidance (2)
Understanding threat sources Understand threats by humans, their motivation
and potential attack modes [27]
Understand threats due to system faults and failures
Example design guidelines for preventing threats: Model for secure protocols [15]
Formal models for analysis of authentication protocols [25, 10]
Models for statistical databases to prevent data disclosures [1]
ICDCIT 2004 24
Dealing with Threats – Threat Tolerance
Useful features of fault-tolerant approach Not concerned with each individual failure Don’t spend all resources on dealing with individual failures Can ignore transient and non-catastrophic errors and failures
Need analogous intrusion-tolerant approach Deal with lesser and common security breaches E.g.: intrusion tolerance for database systems [3]
Phase 1: attack detection Optional (e.g., majority voting schemes don’t need detection)
Phases 2-5: damage confinement, damage assessment, reconfiguration, continuation of service
can be implicit (e.g., voting schemes follow the same procedure whether attacked or not)
Phase 6: report attack to repair and fault treatment (to prevent a recurrence of similar
attacks)
ICDCIT 2004 25
Dealing with Threats – Fraud Threat Detection for Threat Tolerance
Fraud threat identification is needed
Fraud detection systems Widely used in telecommunications, online
transactions, insurance Effective systems use both fraud rules and pattern
analysis of user behavior Challenge: a very high false alarm rate
Due to the skewed distribution of fraud occurrences
ICDCIT 2004 26
Fraud Threats
Analyze salient features of fraud threats Some salient features of fraud threats [9]
Fraud is often a malicious opportunistic reaction Fraud escalation is a natural phenomenon Gang fraud can be especially damaging
Gang fraudsters can cooperate in misdirecting suspicion on others
Individuals/gangs planning fraud thrive in fuzzy environments
Use fuzzy assignments of responsibilities to participating entities
Powerful fraudsters create environments that facilitate fraud
E.g.: CEO’s involved in insider trading
ICDCIT 2004 27
Threat Research Issues (1)
Analysis of known threats in context Identify (in metabases) known threats relevant for the
context Find salient features of these threats and associations
between them Threats can be associated also via their links to related
vulnerabilities Infer threat features from features of vulnerabilities related to
them Build a threat taxonomy for the considered context Propose qualitative and quantitative models of threats in
context Including lifecycle threat models
Define measures to determine threat levels
Devise techniques for avoiding/tolerating threats via unpredictability or non-determinism
Detecting known threats Discovering unknown threats
ICDCIT 2004 28
Threat Research Issues (2)
Develop quantitative threat models using analogies to reliability models
E.g., rate threats or attacks using time and effort random variables
Describe the distribution of their random behavior Mean Effort To security Failure (METF)
Analogous to Mean Time To Failure (MTTF) reliability measure Mean Time To Patch and Mean Effort To Patch (new security
measures) Analogous to Mean Time To Repair (MTTR) reliability measure and
METF security measure, respectively
Propose evaluation methods for threat impacts Mere threat (a potential for attack) has its impact Consider threat properties: direct damage, indirect damage,
recovery cost, prevention overhead Consider interaction with other threats and defensive
mechanisms
ICDCIT 2004 29
Threat Research Issues (3)
Invent algorithms, methods, and design guidelines to reduce number and severity of threats Consider injection of unpredictability or uncertainty
to reduce threats E.g., reduce data transfer threats by sending portions of
critical data through different routes Investigate threats to security mechanisms
themselves
Study threat detection It might be needed for threat tolerance Includes investigation of fraud threat detection
ICDCIT 2004 30
Products, Services and Research Programs for Industry (1)
There are numerous commercial products and services, and some free products and servicesExamples follow.
Notation used below: Product (Organization)
Example vulnerability and incident metabases CVE (Mitre), ICAT (NIST), OSVDB (osvdb.com), Apache Week Web Server (Red Hat), Cisco
Secure Encyclopedia (Cisco), DOVESComputer Security Laboratory (UC Davis), DragonSoft Vulnerability Database (DragonSoft Security Associates), Secunia Security Advisories (Secunia), SecurityFocus Vulnerability Database (Symantec), SIOS (Yokogawa Electric Corp.), Verletzbarkeits-Datenbank (scip AG), Vigil@nce AQL (Alliance Qualité Logiciel)
Example vulnerability notification systems CERT (SEI-CMU), Cassandra (CERIAS-Purdue), ALTAIR (esCERT-UPC), DeepSight Alert
Services (Symantec), Mandrake Linux Security Advisories (MandrakeSoft)
Example other tools (1) Vulnerability Assessment Tools (for databases, applications, web applications, etc.)
AppDetective (Application Security), NeoScanner@ESM (Inzen), AuditPro for SQL Server (Network Intelligence India Pvt. Ltd.), eTrust Policy Compliance (Computer Associates), Foresight (Cubico Solutions CC), IBM Tivoli Risk Manager (IBM), Internet Scanner (Internet Security Systems), NetIQ Vulnerability Manager (NetIQ), N-Stealth (N-Stalker), QualysGuard (Qualys), Retina Network Security Scannere (Eye Digital Security), SAINT (SAINT Corp.), SARA (Advanced Research Corp.), STAT-Scanner (Harris Corp.), StillSecure VAM (StillSecure), Symantec Vulnerability Assessment (Symantec)
Automated Scanning Tools, Vulnerability Scanners Automated Scanning (Beyond Security Ltd.), ipLegion/intraLegion (E*MAZE Networks), Managed
Vulnerability Assessment (LURHQ Corp.), Nessus Security Scanner (The Nessus Project), NeVO (Tenable Network Security)
ICDCIT 2004 31
Products, Services and Research Programs for Industry (2)
Example other tools (2) Vulnerability und Penetration Testing
Attack Tool Kit (Computec.ch), CORE IMPACT (Core Security Technologies), LANPATROL (Network Security Syst.)
Intrusion Detection System Cisco Secure IDS (Cisco), Cybervision Intrusion Detection System (Venus Information Technology),
Dragon Sensor (Enterasys Networks), McAfee IntruShield (IDSMcAfee), NetScreen-IDP (NetScreen Technologies), Network Box Internet Threat Protection Device (Network Box Corp.)
Threat Management Systems Symantec ManHunt (Symantec)
Example services Vulnerability Scanning Services
Netcraft Network Examination Service (Netcraft Ltd.) Vulnerability Assessment and Risk Analysis Services
ActiveSentry (Intranode), Risk Analysis Subscription Service (Strongbox Security), SecuritySpace Security Audits (E-Soft), Westpoint Enterprise Scan (Westpoint Ltd.)
Threat Notification TruSecure IntelliSHIELD Alert Manager (TruSecure Corp.)
Pathches Software Security Updates (Microsoft)
More on metabases/tools/services: http://www.cve.mitre.org/compatible/product.html
Example Research Programs Microsoft Trustworthy Computing (Security, Privacy, Reliability, Business Integrity) IBM
Almaden: information security; Zurich: information security, privacy, and cryptography; Secure Systems Department; Internet Security group; Cryptography Research Group
ICDCIT 2004 32
Outline
1. Vulnerabilities
2. Threats
3. Mechanisms to Reduce Vulnerabilities and Threats3.1.Applying Reliability and Fault Tolerance
Principles to Security Research 3.2.Using Trust in Role-based Access
Control 3.3.Privacy-preserving Data Dissemination 3.4.Fraud Countermeasure Mechanisms
ICDCIT 2004 33
Applying Reliability Principlesto Security Research (1)
Apply the science and engineering from Reliability to Security [6]
Analogies in basic notions [6, 7] Fault – vulnerability Error (enabled by a fault) – threat (enabled by a vulnerability) Failure/crash (materializes a fault, consequence of an error) –
Security breach (materializes a vulnerability, consequence of a threat)
Time - effort analogies: [18]
time-to-failure distribution for accidental failures –expended effort-to-breach distribution for intentional security breaches
This is not a “direct” analogy: it considers important differences between Reliability and Security
Most important: intentional human factors in Security
ICDCIT 2004 34
Applying Reliability Principlesto Security Research (2)
Analogies from fault avoidance/tolerance [27]
Fault avoidance - threat avoidance Fault tolerance - threat tolerance (gracefully adapts to
threats that have materialized) Maybe threat avoidance/tolerance should be named: vulnerability
avoidance/tolerance(to be consistent with the vulnerability - fault analogy)
Analogy:To deal with failures, build fault-tolerant systems
To deal with security breaches, build threat-tolerant systems
ICDCIT 2004 35
Applying Reliability Principlesto Security Research (3)
Examples of solutions using fault tolerance analogies Voting and quorums
To increase reliability - require a quorum of voting replicasTo increase security - make forming voting quorums more difficult
This is not a “direct” analogy but a kind of its “reversal”
Checkpointing applied to intrusion detection To increase reliability – use checkpoints to bring system back to
a reliable (e.g., transaction consistent) state To increase security - use checkpoints to bring system back to a
secure state
Adaptability / self-healing Adapt to common and less severe security breaches as we adapt
to every-day and relatively benign failures Adapt to: timing / severity / duration / extent of a security
breach
ICDCIT 2004 36
Applying Reliability Principlesto Security Research (4)
Beware: Reliability analogies are not always helpful Differences between seemingly identical notions
E.g., “system boundaries” are less open for Reliability than for Security
No simple analogies exist for intentional security breaches arising from planted malicious faults
In such cases, analogy of time (Reliability) to effort (Security) is meaningless
E.g., sequential time vs. non-sequential effort E.g., long time duration vs. “nearly instantaneous” effort
No simple analogies exist when attack efforts are concentrated in time
As before, analogy of time to effort is meaningless
ICDCIT 2004 37
Outline
1. Vulnerabilities
2. Threats
3. Mechanisms to Reduce Vulnerabilities and Threats3.1.Applying Reliability and Fault
Tolerance Principles to Security Research
3.2.Using Trust in Role-based Access Control
3.3.Privacy-preserving Data Dissemination 3.4.Fraud Countermeasure Mechanisms
ICDCIT 2004 38
Basic Idea - Using Trust in Role-based Access Control (RBAC)
Traditional identity-based approaches to access control are inadequate Don’t fit open computing, incl. Internet-based
computing [28]
Idea: Use trust to enhance user authentication and authorization Enhance role-based access control (RBAC) Use trust in addition to traditional credentials
Trust based on user behavior
Trust is related to vulnerabilities and threats Trustworthy users:
Don’t exploit vulnerabilities Don’t become threats
ICDCIT 2004 39
Overview - Using Trust in RBAC (1)
Trust-enhanced role-mapping (TERM) server added to a system with RBAC
Collect and use evidence related to trustworthiness of user behavior Formalize evidence type, evidence
Different forms of evidence must be accommodated Evidence statement: incl. evidence and opinion
Opinion tells how much the evidence provider trust the evidence he provides
ICDCIT 2004 40
Overview - Using Trust in RBAC (2) TERM architecture includes:
Algorithm to evaluate credibility of evidence Based on its associated opinion and evidence about
trustworthiness of the opinion issuer’s Declarative language to define role assignment
policies Algorithm to assign roles to users
Based on role assignment policies and evidence statements
Algorithm to continuously update trustworthiness ratings for user
Its output is used to grant or disallow access request Trustworthiness ratings for a recommender is affected by
trustworthiness ratings of all users he recommended
ICDCIT 2004 41
Overview - Using Trust in RBAC (3)
A prototype TERM server Software available at:
http://www.cs.purdue.edu/homes/bb/NSFtrust.html
More details on “Using Trust in RBAC”available in the extended version of this presentation
at: www.cs.purdue.edu/people/bb#colloquia
ICDCIT 2004 42
Access Control: RBAC & TERM Server
Role-based access control (RBAC)
Trust-enhanced role-mapping (TERM) server cooperates with RBAC
userTERM Server
Send roles
RBAC enhanced Web Server
Request roles
Request A
ccess
Respond
ICDCIT 2004 43
Evidence
Evidence: Direct evidence
User/issuer behavior observed by TERM First-hand information
OR: Indirect evidence (recommendation)
Recommender ’s opinion w.r.t. trust in a user/issuer Second-hand information
ICDCIT 2004 44
Evidence Model
Design considerations: Accommodate different forms of evidence in an integrated
framework Support credibility evaluation
Evidence type Specify information required by this evidence type (et) (et_id, (attr_name, attr_domain, attr_type)* ) E.g.: (student, [{name, string, mand}, {university, string, mand}, {department, string, opt}])
Evidence Evidence is an instance of an evidence type
ICDCIT 2004 45
Evidence Model – cont.
Opinion (belief, disbelief, uncertainty) Probability expectation of Opinion
Belief + 0.5 * uncertainty Characterizes the degree of trust represented by an
opinion Alternative representation
Fuzzy expression Uncertainty vs. vagueness
Evidence statement <issuer, subject, evidence, opinion>
ICDCIT 2004 46
TERM Server Architecture
assigned roles
users’ behaviors
credentialmgmt
role-assignment policies specified by system administrators
credentials provided by third parties or retrieved from the internet
role assignment
evidencestatement
evidence statement, credibility
evidenceevaluationissuer’s trust
user/issuer information database
user’s trust trust
informationmgmt
Component implemented
Component partially implemented
Credential Management (CM) – transforms different formats of credentials to evidence statements Evidence Evaluation (EE) - evaluates credibility of evidence statements Role Assignment (RA) - maps roles to users based on evidence statements and role assignment policies Trust Information Management (TIM) - evaluates user/issuer’s trust information based on direct experience
and recommendations
ICDCIT 2004 47
EE - Evidence Evaluation
Develop an algorithm to evaluate credibility of evidence Issuer’s opinion cannot be used as credibility of
evidence
Two types of information used: Evidence Statement
Issuer’s opinion Evidence type
Trust w.r.t. issuer for this kind of evidence type
ICDCIT 2004 48
EE - Evidence Evaluation Algorithm
Input: evidence statement E1 = <issuer, subject, evidence, opinion1>
Output: credibility RE(E1) of evidence statement E1
Step1: get opinion1 = <b1, d1, u1> and issuer field from evidence statement E1
Step2: get the evidence statement about issuer’s testimony_trust E2 =
<term_server, issuer, testimony_trust, opinion2> from local database
Step3: get opinion2 = <b2, d2, u2> from evidence statement E2
Step4: compute opinion3 = <b3, d3, u3 >
(1) b3 = b1 * b2
(2) d3 = b1 * d2
(3) u3 = d1 + u1 + b2 * u1
Step5: compute probability expectation for opinion3 = < b3, d3, u3 >
PE (opinion3) = b3 + 0.5 * u3
Step6: RE (E1) = PE (opinion3)
ICDCIT 2004 49
RA - Role Assignment
Design a declarative language for system administrators to define role assignment policies
Specify content and number of evidence statements needed for role assignment
Define a threshold value characterizing the minimal degree of trust expected for each evidence statement
Specify trust constraints that a user/issuer must satisfy to obtain a role
Develop an algorithm to assign roles based on policies Several policies may be associated with a role
The role is assigned if one of them is satisfied
A policy may contain several unitsThe policy is satisfied if all units evaluate to True
ICDCIT 2004 50
RA - Algorithm for Policy Evaluation
Input: evidence set E and their credibility, role AOutput: true/false
P ← the set of policies whose left hand side is role Awhile P is not empty{
q = a policy in Psatisfy = truefor each units u in q{if evaluate_unit(u, e, re(e)) = false for all evidence
statements e in E satisfy = false
}if satisfy = true
return trueelse
remove q from P }return false
ICDCIT 2004 51
RA - Algorithm for Unit Evaluation
Input: evidence statement E1 = <issuer, subject, evidence, opinion1> and its credibility RE (E1), a unit of policy U
Output: true/false
Step1: if issuer does not hold the IssuerRole specified in U or the type of evidence does not match evidence_type in U then return false
Step2: evaluate Exp of U as follows: (1) if Exp1 = “Exp2 || Exp3” then
result(Exp1) = max(result(Exp2), result(Exp3))(2) else if Exp1 = “Exp2 && Exp3” then
result(Exp1) = min(result(Exp2), result(Exp3))(3) else if Exp1 = “attr Op Constant” then
if Op {EQ, GT, LT, EGT, ELT} then if “attr Op Constant” = true then result(Exp1) = RE(E1) else result(Exp1) = 0
else if Op = NEQ” then if “attr Op Constant” = true then result(Exp1) = RE(E1) else result(Exp1) = 1- RE(E1)
Step3: if min(result(Exp), RE(E1)) threshold in U then output true else output false
ICDCIT 2004 52
TIM - Trust Information Management
Evaluate “current knowledge” “Current knowledge:”
Interpretations of observations Recommendations
Developed algorithm that evaluates trust towards a user
User’s trustworthiness affects trust towards issuers who introduced user
Predict trustworthiness of a user/issuer Current approach uses the result of
evaluation as the prediction
ICDCIT 2004 53
Defining role assignment policies Loading evidence for role assignment
Software: http://www.cs.purdue.edu/homes/bb/NSFtrust.html
Prototype TERM Server
ICDCIT 2004 54
Outline
1. Vulnerabilities
2. Threats
3. Mechanisms to Reduce Vulnerabilities and Threats3.1.Applying Reliability and Fault
Tolerance Principles to Security Research
3.2.Using Trust in Role-based Access Control
3.3.Privacy-preserving Data Dissemination 3.4.Fraud Countermeasure Mechanisms
ICDCIT 2004 55
Basic Terms - Privacy-preserving Data Dissemination
“Guardian:”Entity entrusted by private data owners with collection, storage, or transfer of their data
owner can be a guardian for its own private data owner can be an institution or a computing system
Guardians allowed or required by law to share private data With owner’s explicit consent Without the consent as required by law
research, court order, etc.
“Data”(Private Data)
Guardian 2Second Level
Guardian 1 Original Guardian
Guardian 3
Guardian 5Third-level
Guardian 6Guardian 4
“Owner”(Private Data
Owner)
ICDCIT 2004 56
Problem of Privacy Preservation
Guardian passes private data to another guardian in a data dissemination chain Chain within a graph (possibly cyclic)
Owner privacy preferences not transmitted due to neglect or failure Risk grows with chain length and milieu
fallibility and hostility
If preferences lost, receiving guardian unable to honor them
ICDCIT 2004 57
Challenges
Ensuring that owner’s metadata are never decoupled from his data Metadata include owner’s privacy preferences
Efficient protection in a hostile milieu Threats - examples
Uncontrolled data dissemination Intentional or accidental data corruption, substitution,
or disclosure Detection of a loss of data or metadata Efficient recovery of data and metadata
Recovery by retransmission from the original guardian is most trustworthy
ICDCIT 2004 58
Overview - Privacy-preserving Data Dissemination
Use bundles to make data and metadata inseparablebundle = self-descriptive private data + its metadata
E.g., encrypt or obfuscate bundle to prevent separation
Each bundle includes mechanism for apoptosis apoptosis = clean self-destruction
Bundle chooses apoptosis when threatened with a successful hostile attack
Develop distance-based evaporation of bundles E.g., the more “distant” from it owner is a bundle, the
more it evaporates (becoming more distorted)
More details on “Privacy-preserving Data Dissemination” available in the extended version of
this presentationat: www.cs.purdue.edu/people/bb#colloquia
ICDCIT 2004 59
Proposed Approach
A. Design bundlesbundle = self-descriptive private data + its
metadata
B. Construct a mechanism for apoptosis of bundlesapoptosis = clean self-destruction
C. Develop distance-based evaporation of bundles
ICDCIT 2004 60
Related Work
Self-descriptiveness Many papers use the idea of self-descriptiveness in
diverse contexts (meta data model, KIF, context-aware mobile infrastructure, flexible data types)
Use of self-descriptiveness for data privacy The idea briefly mentioned in [Rezgui, Bouguettaya,
and Eltoweissy, 2003]
Securing mobile self-descriptive objects Esp. securing them via apoptosis, that is clean self-
destruction [Tschudin, 1999]
Specification of privacy preferences and policies Platform for Privacy Preferences [Cranor, 2003] AT&T Privacy Bird [AT&T, 2004]
ICDCIT 2004 61
A. Self-descriptiveness in Bundles
Comprehensive metadata include:
owner’s privacy preferences
owner’s contact information
guardian privacy policies
metadata access conditions
enforcement specifications
data provenance
context-dependent andother components
How to read and write private data
For the original and/or subsequent data guardians
How to verify and modify metadata
How to enforce preferences and policies
Who created, read, modified, or destroyed any portion of data
Application-dependent elementsCustomer trust levels for different contexts
Other metadata elements
To notify or request permission
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Bundle Owner Notification
Bundles simplify notifying owners or requesting their permissions Contact information available in owner’s
contact information component
Notifications and requests sent to owners immediately, periodically, or on owner’s demand Via pagers, SMSs, email, mail, etc.
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Transmitting complete bundles between guardians is inefficient Metadata in bundles describe all foreseeable
aspects of data privacy For any application and environment
Solution: prune transmitted metadata Use application and environment semantics
along the data dissemination chain
Optimization of Bundle Transmission
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B. Apoptosis of Bundles
Assuring privacy in data dissemination In benevolent settings:
use atomic bundles with retransmission recovery In malevolent settings:
when attacked bundle threatened with disclosure, it uses apoptosis (clean self-destruction)
Implementation Detectors, triggers, code False positive
Dealt with by retransmission recovery Limit repetitions to prevent denial-of-service attacks
False negatives
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C. Distance-based Evaporationof Bundles
Perfect data dissemination not always desirable Example: Confidential business data may be
shared withinan office but not outside
Idea: Bundle evaporate in proportion toits “distance” from its owner
“Closer” guardians trusted more than “distant” ones Illegitimate disclosures more probable at less trusted
“distant” guardians Different distance metrics
Context-dependent
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Examples of one-dimensional distance metrics Distance ~ business type
Distance ~ distrust level: more trusted entities are “closer”
Multi-dimensional distance metrics Security/reliability as one of dimensions
Examples of Metrics
Insurance Company
B
5
1
5
5
2
2
1
2
Bank I -Original Guardia
n
Insurance Company
C
Insurance Company A
Bank II
Bank III
Used Car
Dealer 1
Used Car
Dealer 2
Used Car
Dealer 3
If a bank is the original guardian, then:-- any other bank is “closer” than any insurance company-- any insurance company is “closer” than any used car dealer
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Distorted data reveal less, protecting privacy Examples:
accurate more and more distorted
Evaporation Implemented asControlled Data Distortion
250 N. Salisbury StreetWest Lafayette, IN
250 N. Salisbury StreetWest Lafayette, IN[home address]
765-123-4567[home phone]
Salisbury StreetWest Lafayette, IN
250 N. University StreetWest Lafayette, IN[office address]
765-987-6543[office phone]
somewhere inWest Lafayette, IN
P.O. Box 1234West Lafayette, IN[P.O. box]
765-987-4321 [office fax]
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Outline
1. Vulnerabilities
2. Threats
3. Mechanisms to Reduce Vulnerabilities and Threats3.1.Applying Reliability and Fault
Tolerance Principles to Security Research
3.2.Using Trust in Role-based Access Control
3.3.Privacy-preserving Data Dissemination 3.4.Fraud Countermeasure Mechanisms
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Overview - Fraud Countermeasure Mechanisms (1)
System monitors user behavior
System decides whether user’s behavior qualifies as fraudulent
Three types of fraudulent behavior identified: “Uncovered deceiving intention”
User misbehaves all the time
“Trapping intention” User behaves well at first, then commits fraud
“Illusive intention” User exhibits cyclic behavior: longer periods of proper
behavior separated by shorter periods of misbehavior
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Overview - Fraud Countermeasure Mechanisms (2)
System architecture for swindler detection Profile-based anomaly detector
Monitors suspicious actions searching for identified fraudulent behavior patterns
State transition analysis Provides state description when an activity results in
entering a dangerous state Deceiving intention predictor
Discovers deceiving intention based on satisfaction ratings Decision making
Decides whether to raise fraud alarm when deceiving pattern is discovered
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Overview - Fraud Countermeasure Mechanisms (3)
Performed experiments validated the architecture All three types of fraudulent behavior were quickly
detected
More details on “Fraud Countermeasure Mechanisms”
available in the extended version of this presentation
at: www.cs.purdue.edu/people/bb#colloqia
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Formal Definitions
A swindler – an entity that has no intention to keep his commitment in cooperation
Commitment: conjunction of expressions describing an entity’s promise in a process of cooperation Example: (Received_by=04/01) (Prize=$1000)
(Quality=“A”) ReturnIfAnyQualityProblem Outcome: conjunction of expressions
describing the actual results of a cooperation Example: (Received_by=04/05) (Prize=$1000)
(Quality=“B”) ¬ReturnIfAnyQualityProblem
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Formal Definitions
Intention-testifying indicates a swindler Predicate P: ¬P in an outcome entity making the
promise is a swindler Attribute variable V: V's expected value is more
desirable than the actual value the entity is a swindler Intention-dependent indicates a possibility
Predicate P: ¬P in an outcome entity making the promise may be a swindler
Attribute variable V: V's expected value is more desirable than the actual value the entity may be a swindler
An intention-testifying variable or predicate is intention-dependentThe opposite is not necessarily true
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Modeling Deceiving Intentions (1)
Satisfaction rating Associate it with the actual value of each intention-
dependent variable in an outcome
Range: [0,1] The higher the rating, the more satisfied the user
Related to deceiving intention and unpredictable factors
Modeled by using random variable with normal distribution
Mean function fm(n) Mean value of normal distribution for n-th rating
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Modeling Deceiving Intentions (2)
Uncovered deceiving intention Satisfaction ratings
are stably low
Ratings vary in a small range over time
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Modeling Deceiving Intentions (3)
Trapping intention Rating sequence has
two phases:preparing and trapping
A swindler initially behaves (well to achieve a trustworthy image), then conducts frauds
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Modeling Deceiving Intentions (4)
Illusive intention A smart swindler
attempts to “cover” bad behavior by intentionally doing something good after misbehaviors
Preparing and trapping phases are repeated cyclically
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Architecture for Swindler Detection (1)
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Architecture for Swindler Detection (2)
Profile-based anomaly detector Monitors suspicious actions based upon the
established behavior patterns of an entity
State transition analysis Provides state description when an activity results
in entering a dangerous state
Deceiving intention predictor Discovers deceiving intention based on
satisfaction ratings
Decision making
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Profile-based Anomaly Detector (1)
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Profile-based Anomaly Detector (2)
Rule generation and weighting Generate fraud rules and weights associated with
rules
User profiling Variable selection Data filtering
Online detection Retrieve rules when an activity occurs Retrieve current and historical behavior patterns Calculate deviation between these two patterns
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Deceiving Intention Predictor
Kernel of the predictor: DIP algorithm
Belief for deceiving intention as complementary of trust belief
Trust belief evaluated based on satisfaction sequence
Trust belief properties: Time dependent Trustee dependent Easy-destruction-hard-constructio
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Experimental Study
Goal:Investigate DIP’s capability of discovering deceiving intentions
Initial values for parameters: Construction factor (Wc): 0.05 Destruction factor (Wd): 0.1 Penalty ratios for construction factor (r1): 0.9 Penalty ratios for destruction factor (r2): 0.1 Penalty ratios for supervision-period (r3): 2 Threshold for a foul event (fThreshold): 0.18
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Discover Swindler with Uncovered Deceiving Intention
Trust values are close to the minimum rating of interactions: 0.1
Deceiving intention belief is high, around 0.9
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Discover Swindler with Trapping Intention
DIP responds quickly to sharp drop in behavior “goodness”
It takes 6 interactions for DI-confidence to increase from 0.2239 to 0.7592 after the sharp drop
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Discover Swindler with Illusive Intention
DIP is able to catch this smart swindler because belief in deceiving intention eventually increases to about 0.9
The swindler's effort to mask his fraud with good behaviors has less and less effect with each next fraud
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Conclusions for Fraud Detection
Define concepts relevant to frauds conducted by swindlers
Model three deceiving intentions Propose an approach for swindler
detection and an architecture realizing the approach
Develop a deceiving intention prediction algorithm
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Summary
Presented:1. Vulnerabilities2. Threats3. Mechanisms to Reduce Vulnerabilities and
Threats3.1. Applying Reliability and Fault Tolerance Principles to
Security Research 3.2. Using Trust in Role-based Access Control 3.3. Privacy-preserving Data Dissemination 3.4. Fraud Countermeasure Mechanisms
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Conclusions
Exciting area of research 20 years of research in Reliability can form a
basis for vulnerability and threat studies in Security
Need to quantify threats, risks, and potential impacts on distributed applications. Do not be terrorized and act scared
Adapt and use resources to deal with different threat levels
Government, industry, and the public are interested in progress in this research
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The extended version of this presentation available at: www.cs.purdue.edu/people/bb#colloqia
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Thank you!