Intelligent jamming using deep q-learning

Noah Thurston, Garrett Vanhoy, Tamal Bose

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The 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.

Original languageEnglish (US)
Title of host publication54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018
Subtitle of host publicationReliable and Secure Data, Links and Networks
PublisherInternational Foundation for Telemetering
ISBN (Electronic)9780000000002
StatePublished - Jan 1 2018
Event54th Annual International Telemetering Conference and Technical Exhibition: Reliable and Secure Data, Links and Networks, ITC 2018 - Glendale, United States
Duration: Nov 5 2018Nov 8 2018

Publication series

NameProceedings of the International Telemetering Conference
Volume2018-November
ISSN (Print)0884-5123

Conference

Conference54th Annual International Telemetering Conference and Technical Exhibition: Reliable and Secure Data, Links and Networks, ITC 2018
CountryUnited States
CityGlendale
Period11/5/1811/8/18

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation
  • Computer Networks and Communications
  • Signal Processing

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  • Cite this

    Thurston, N., Vanhoy, G., & Bose, T. (2018). Intelligent jamming using deep q-learning. In 54th Annual International Telemetering Conference and Technical Exhibition, ITC 2018: Reliable and Secure Data, Links and Networks (Proceedings of the International Telemetering Conference; Vol. 2018-November). International Foundation for Telemetering.