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dc.contributor.authorThurston, Noah
dc.contributor.authorVanhoy, Garrett
dc.contributor.authorBose, Tamal
dc.date.accessioned2019-02-11T23:15:39Z
dc.date.available2019-02-11T23:15:39Z
dc.date.issued2018-11
dc.identifier.issn0884-5123
dc.identifier.issn0074-9079
dc.identifier.urihttp://hdl.handle.net/10150/631658
dc.description.abstractThe threat of a malicious user interfering with network traffic so as to deny access to resources is an inherent vulnerability of wireless networks. To combat this threat, physical layer waveforms that are resilient to interference are used to relay critical traffic. These waveforms are designed to make it difficult for a malicious user to both deny access to network resources and avoid detection. If a malicious user has perfect knowledge of the waveform being used, it can avoid detection and deny network throughput, but this knowledge is naturally limited in practice. In this work, the threat of a malicious user that can implicitly learn the nature of the waveform being used simply by observing reactions to its behavior is analyzed and potential mitigation techniques are discussed. The results show that using recurrent neural networks to implement deep Q-learning, a malicious user can converge on an optimal interference policy that simultaneously minimizes the potential for it to be detected and maximizes its impediment on network traffic.en_US
dc.description.sponsorshipInternational Foundation for Telemeteringen_US
dc.language.isoen_USen_US
dc.publisherInternational Foundation for Telemeteringen_US
dc.relation.urlhttp://www.telemetry.org/en_US
dc.rightsCopyright © held by the author; distribution rights International Foundation for Telemetering
dc.titleINTELLIGENT JAMMING USING DEEP Q-LEARNINGen_US
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen_US
dc.identifier.journalInternational Telemetering Conference Proceedingsen_US
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.
refterms.dateFOA2019-02-11T23:15:40Z


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