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Multi-Source Fusion for Homeland Terrorist Threat Detection

This paper presents a technique for the opportunistic detection and probabilistic assessment of homeland terrorist threats by fusing multiple sources of information. The technique utilizes Bayesian network fragments and a situation-specific network construction process to reason about specific target hypotheses based on evidence. The use of Multi-Entity Bayesian Networks (MEBN) allows for the linking of standardized information sources and enables a faster response to widely dispersed but related events.

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Multi-Source Fusion for Homeland Terrorist Threat Detection

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  1. Multisource Fusion for Opportunistic Detection and ProbabilisticAssessment of Homeland Terrorist ThreatsKathryn Blackmond Laskey& Tod S. Levittpresented by Viji Avali & Richard McCrawCSCE 582 Fall02Instructor: Dr. Marco Valtorta

  2. Introduction Terrorist attacks on 9/11 caused growing array of information about • possible sources • and alerts To use these effectively fusion of these sources is essential Ex: Biological Warfare attacks

  3. Definitions • Bayesian Network Fragments • Basic unit. The network fragment consists of a set of related variables together with knowledge about the probabilistic relationships among the variables. • Multi Entity Bayesian Network (MEBN) • Collection of BNFrags specifying probability distribution over attributes of and relationships among a collection of interrelated entities.

  4. Definitions • Situation-Specific Network(SSN) • Ordinary finite Bayesian Network constructed from an MEBN knowledge base, to reason about specific target hypothesis, with a particular evidence.

  5. SSNconstructionprocess • Clusters of reports trigger firing of a suggestor. Suggestors are rules that use the given situation to decide which hypotheses need to be represented. • Suggestors trigger retrieval of relevant BNFrags. • Variables in the BNFrag are replaced by actual entities in the situations.

  6. SSN Construction Process- Contd.. • Current SSN is created combining these BNFrags and already existing SSN (if any). • Evidence is applied and inferences are drawn about the hypotheses. • Desired actions are decided by evaluating the decision nodes.

  7. Comments There are couple of things to note about the technique in this paper. • When the SSN is constructed,the parts of the model not needed to reason from the evidence to the target variables are removed. This may be done by using techniques like relevance reasoning, nuisance node removal. • When two SSN’s are combined, if the resultantnetwork has new parent nodes introduced, the combined probability of the already existing node(s) and the new node(s) should be already available to evaluate the combined results.

  8. Conclusion • A MEBN allows for the linking of standardized information sources. • The MEBN will now pull from the entire knowledge base on a certain target hypothesis. • This allows for a faster response to widely dispersed, but related events.

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