Confidentiality and Trust Management in a Coalition Environment Lecture #11 Dr. Bhavani Thuraisingham February 13, 2008Acknowledgements: AFOSR Funded ProjectArchitectureOur ApproachData Sharing, Miner and AnalyzerData Partitioning and PoliciesExample PoliciesProcessingExtension For Trust ManagementRole-based Usage Control (RBUC)RBUC in Coalition EnvironmentCoalition Game TheorySlide 13Detecting Malicious Executables The New Hybrid ModelCurrent Directions1Confidentiality and Trust Management in a Coalition EnvironmentLecture #11Dr. Bhavani ThuraisinghamFebruary 13, 2008Data and Applications SecurityDevelopments and Directions2Acknowledgements: AFOSR Funded ProjectStudents-UTDallasDilsad Cavus (MS, Data mining and data sharing)Srinivasan Iyer (MS, Trust management)Ryan Layfield (PhD, Game theory)Mehdi (PhD, Worm detection)-GMUMin (PhD, Extended RBAC)Faculty and Staff-UTDallasProf. Khan (Co-PI), Prof. Murat (Game theory)Dr. Mamoun Awad (Data mining and Data sharing)GMU: Prof. Ravi Sandhu3ArchitectureExportData/PolicyComponentData/Policy for Agency AData/Policy for FederationExportData/PolicyComponentData/Policy for Agency CComponentData/Policy for Agency BExportData/Policy4Our ApproachIntegrate the Medicaid claims data and mine the data; next enforce policies and determine how much information has been lost by enforcing policiesExamine RBAC and UCON in a coalition environmentApply game theory and probing techniques to extract information from non cooperative partners; conduct information operations and determine the actions of an untrustworthy partner. Defensive and offensive operations5Data Sharing, Miner and AnalyzerAssume N organizations.-The organizations don’t want to share what they have.-They hide some information.-They share the rest. Simulates N organizations which -Have their own policies-Are trusted partiesCollects data from each organization,-Processes it,-Mines it,-Analyzes the results6Data Partitioning and PoliciesPartitioning-Horizontal: Has all the records about some entities-Vertical: Has subset of the fields of all entities-Hybrid: Combination of Horizontal and Vertical partitioningPolicies-XML document-Informs which attributes can be releasedRelease factor: -Is the percentage of attributes which are released from the dataset by an organization.-A dataset has 40 attributes.“Organization 1” releases 8 attributesRF=8/40=20%7Example Policies8Processing1. Load and Analysis. -loads the generated rules,-analyzes them, -displays in the charts.2. Run ARM. -chooses the arff file -Runs the Apriori algorithm,-displays the association rules, frequent item sets and their confidences.3. Process DataSet: -Processes the dataset using Single Processing or Batch Processing.9Extension For Trust ManagementEach Organization maintains a Trust Table for Other organization.The Trust level is managed based on the quality of Information.Minimum Threshold- below which no Information will be shared. Maximum Threshold - Organization is considered Trusted partner.10Role-based Usage Control (RBUC)RBAC with UCON extension11RBUC in Coalition Environment•The coalition partners maybe trustworthy), semi-trustworthy) or untrustworthy), so we can assign different roles on the users (professor) from different infospheres, e.g.•professor role, •trustworthy professor role, •semi-trustworthy professor role,•untrustworthy professor role.•We can enforce usage control on data by set up object attributes to different roles during permission-role-assignment, •e.g. professor role: 4 times a day,trustworthy role: 3 times a daysemi-trustworthy professor role: 2 times a day,untrustworthy professor role: 1 time a day12Coalition Game TheoryLieTellTruthLieTell TruthPjPiAA) )v e r i f y((ijpMB) )f a k e(1(ijpLA) )f a k e(1(jipLA) )v e r i f y((jipMB) )f a k e(1() )v e r i f y((jiijpLpMB) )f a k e(1() )v e r i f y((ijjipLpMBA = Value expected from telling the truthB = Value expected from lying M = Loss of value due to discovery of lieL = Loss of value due to being lied to = Percieved probability by player i that player j will perform actionfake: Choosing to lieverify: Choosing to verifyPlayersStrategy for Player iStrategy for Player jExpected Benefitfrom Strategy)a c t i o n(ijp13Coalition Game TheoryResults-Algorithm proved successful against competing agents-Performed well alone, benefited from groups of likeminded agents-Clear benefit of use vs. simpler alternatives-Worked well against multiple opponents with different strategiesPending Work-Analyzing dynamics of data flow and correlate successful patterns-Setup fiercer competition among agentsTit-for-tat AlgorithmAdaptive Strategy Algorithm (a.k.a. Darwinian Game Theory)Randomized Strategic Form-Consider long-term gamesData gathered carries into next gameConsideration of reputation (‘trustworthiness’) necessary14Detecting Malicious Executables The New Hybrid ModelWhat are malicious executables?Virus, Exploit, Denial of Service (DoS), Flooder, Sniffer, Spoofer, Trojan etc.Exploits software vulnerability on a victim, May remotely infect other victims Malicious code detection: approachesSignature based : not effective for new attacksOur approach: Reverse engineering applied to generate assembly code features, gaining higher accuracy than simple byte code features Executable Files Select Best features using Information Gain Byte-Codes n-grams Feature vector(n-byte sequences) Reduced Feature vector(n-byte sequences) Machine-Learning Feature vector(Assembly codeSequences)Replace byte-code with assembly codeMalicious / Benign ? Hex-dump15Current DirectionsDeveloped a plan to implement Information Operations for untrustworthy partners and will start the implementation in February 2007Continuing with the design and implementation of RBUC for CoalitionsEnhancing the game theory based model for semi-trustworthy partnersInvestigate Policy Management for a Need to share
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