Defense Strategies Against Adversarial Jamming Attacks via Deep Reinforcement Learning

Feng Wang, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar

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

1 Scopus citations

Abstract

As the applications of deep reinforcement learning (DRL) in wireless communication grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw more attention. In order to address such sensitivity and alleviate the resulting security concerns, we in this paper study defense strategies against DRL-based jamming attacker on a DRL-based dynamic multichannel access agent. To defend the jamming attacks, we propose three diversified defense strategies: proportional-integral-derivative (PID) control, the use of an imitation attacker and the development of orthogonal policies. We design these strategies and evaluate their performances.

Original languageEnglish (US)
Title of host publication2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728140841
DOIs
StatePublished - Mar 2020
Event54th Annual Conference on Information Sciences and Systems, CISS 2020 - Princeton, United States
Duration: Mar 18 2020Mar 20 2020

Publication series

Name2020 54th Annual Conference on Information Sciences and Systems, CISS 2020

Conference

Conference54th Annual Conference on Information Sciences and Systems, CISS 2020
CountryUnited States
CityPrinceton
Period3/18/203/20/20

Keywords

  • adversarial policies
  • deep reinforcement learning
  • Defense strategies
  • dynamic channel access

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

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  • Cite this

    Wang, F., Zhong, C., Gursoy, M. C., & Velipasalar, S. (2020). Defense Strategies Against Adversarial Jamming Attacks via Deep Reinforcement Learning. In 2020 54th Annual Conference on Information Sciences and Systems, CISS 2020 [9086263] (2020 54th Annual Conference on Information Sciences and Systems, CISS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS48834.2020.1570629719