Assured Information Sharing for Security and Intelligence Applications Prof Bhavani Thuraisingham Prof Latifur Khan Prof Murat Kantarcioglu Prof Kevin Hamlen The University of Texas at Dallas Project Funded by the Air Force Office of Scientific Research AFOSR Collaborator Prof Ravi Sandhu UTSA October 2008 Assured Information Sharing Daniel Wolfe formerly of the NSA defined assured information sharing AIS as a framework that provides the ability to dynamically and securely share information at multiple classification levels among U S allied and coalition forces The DoD s vision for AIS is to deliver the power of information to ensure mission success through an agile enterprise with freedom of maneuverability across the information environment 9 11 Commission report has stated that we need to migrate from a need to know to a need to share paradigm Our objective is to help achieve this vision by defining an AIS lifecycle and developing a framework to realize it Architecture 2005 2008 Data Policy for Coalition Export Data Policy Export Data Policy Export Data Policy Component Data Policy for Agency A Component Data Policy for Agency C Component Data Policy for Agency B Trustworthy Partners Semi Trustworthy Partners Untrustworthy Partners Our Approach Integrate the Medicaid claims data and mine the data next enforce policies and determine how much information has been lost Trustworthy partners Prototype system Trust for Peer to Peer Networks Apply game theory and probing to extract information from semi trustworthy partners Conduct information operations defensive and offensive and determine the actions of an untrustworthy partner Data Mining applied for trustworthy semi trustworthy and untrustworthy partners Policy Enforcement Prototype Dr Mamoun Awad postdoc and students Coalition Architectural Elements of the Prototype Policy Enforcement Point PEP Enforces policies on requests sent by the Web Service Translates this request into an XACML request sends it to the PDP Policy Decision Point PDP Makes decisions regarding the request made by the web service Conveys the XACML request to the PEP Policy Files Policy Files are written in XACML policy language Policy Files specify rules for Targets Each target is composed of 3 components Subject Resource and Action each target is identified uniquely by its components taken together The XACML request generated by the PEP contains the target The PDP s decision making capability lies in matching the target in the request file with the target in the policy file These policy files are supplied by the owner of the databases Entities in the coalition Databases The entities participating in the coalition provide access to their databases UCON Policy Model Prof Ravi Sandu X Min Operations that we need to model Document read by a member Adding removing a member to from the group Adding removing a document to from the group Member attributes Member boolean TS join join time TS leave leave time Document attributes D Member boolean D TS join join time D TS leave leave time Policy model member enroll dis enroll enroll member TS join TS leave null null null enroll True time of join null dis enroll enroll enroll dis enroll authorized to GroupAdmins Initial state Never been a member False time of join time of leave Currently a member Past member State III State II State I enroll disenroll UCON elements Pre Authorization attribute predicates attribute mutability D member D TS join D TS leave Policy model document add remove add null null null True time of join null add remove False time of join time of leave add remove authorized to Group Admins add Initial state Never been a group doc Currently a group doc Past group doc State I State II State III add remove UCON elements Pre Authorization attribute predicates attribute mutability Distributed Information Exchange Ryan Layfield Murat Kantarcioglu Bhavani Thuraisingham Multiple sovereign parties wish to cooperate Each carries pieces of a larger information puzzle Can only succeed at their tasks when cooperating Have little reason to trust or be honest with each other Cannot agree on single impartial governing agent No one party has significant clout over the rest No party innately has perfect knowledge of opponent actions Verification of information incurs a cost Faking information is a possibility Current modern example Bit Torrent Assumes information is verifiable Enforces punishment however through a centralized server Game Theory Studies such interactions through mathematical representations of gain Each party is considered a player The information they gain from each other is considered a payoff Scenario considered a finite repeated game Information exchanged in discrete chunks each round Situation terminates at a finite yet unforeseeable point in the future Actions within the game are to either lie or tell the truth Our Goal All players draw conclusion that telling the truth is the best option Withdrawal Much of the work in this area only considers sticking with available actions I e Tit for tat Mimic other player s moves All players initially play this game with each other Fully connected graph Initial level of trust inherent As time goes on players which deviate are simply cut off Player that is cut off no longer receives payoff from that link Goal Isolate the players which choose to lie The Payoff Matrix Enforcing Honest Choice Repeated games provide opportunity for enforcement Choice of telling the truth must be beneficial The utility payoff of decisions made Note that when Experimental Setup We created an evolutionary game in which players had the option of selecting a more advantageous behavior Available behaviors included Our punishment method Tit for Tat Subtle lie Every 200 rounds behaviors are re evaluated p select ai f ai n f a i i 0 If everyone agrees on a truth telling behavior our goal is achieved Results Conclusions Semi trustworthy partners Experiments confirm our behaviors success Equilibrium of behavior yielded both a homogenous choice of TruthPunish and truth told by all agents Rigorous despite wide fluctuations in payoff Notable Observations Truth telling cliques of mixed behaviors rapidly converged to TruthPunish Cliques however only succeeded when the ratio of like minded helpful agents outweighed benefits of lying periodically Enough agents must use punishment ideology Tit for Tat was the leading competitor Defensive Operations Detecting Malicious Executables using Data Mining