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<article-title>Distributed Intrusion Detection in Partially Observable Markov Decision Processes</article-title>
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<author><a href="mailto:doran@utulsa.edu"><name>Doran Chakraborty</name></a></author>
<aff>Mathematical &amp; Computer Sciences Department, University of Tulsa, Tulsa, Oklahoma, USA</aff>

<author><a href="mailto:sandip@utulsa.edu"><name>Sandip Sen</name></a></author>
<aff>Mathematical &amp; Computer Sciences Department, University of Tulsa, Tulsa, Oklahoma, USA</aff>
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<title>ABSTRACT</title>
<p>The problem of decentralized control occurs frequently in realistic domains where agents have to cooperate to achieve a universal goal. Planning for domain-level joint strategy takes into account the uncertainty of the underlying environment in computing near-optimal joint-strategies that can handle the intrinsic domain uncertainty. However, uncertainty related to agents deviating from the recommended joint-policy is not taken into consideration. We focus on hostile domains, where the goal is to quickly identify deviations from planned behavior by any compromised agents. There is a growing need to develop techniques that enable the system to recognize and recover from such deviations. We discuss the problem from the intruder's perspective and then present a distributed intrusion detection scheme that can detect a particular type of attack.</p>
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