Learning-Based Robust Anomaly Detection in the Presence of Adversarial Attacks

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

Abstract

To address the anomaly detection problem in the presence of noisy sensor observations and probing costs, we in this paper propose a soft actor-critic deep reinforcement learning framework. Moreover, considering adversarial jamming attacks, we design a generative adversarial network (GAN) based framework to identify the jammed sensors. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total number of samples needed for detection. Via simulation results, we demonstrate the performances when soft actor-critic algorithms are sensitive to the probing cost and actively adapt to different environment settings. We analyze the impact of jamming attacks and identify the improvements achieved by GAN-based approach. We further provide comparisons between the performances of the proposed soft actor-critic and conventional actor-critic algorithms.

Original languageEnglish (US)
Title of host publication2022 IEEE Wireless Communications and Networking Conference, WCNC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1206-1211
Number of pages6
ISBN (Electronic)9781665442664
DOIs
StatePublished - 2022
Event2022 IEEE Wireless Communications and Networking Conference, WCNC 2022 - Austin, United States
Duration: Apr 10 2022Apr 13 2022

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2022-April
ISSN (Print)1525-3511

Conference

Conference2022 IEEE Wireless Communications and Networking Conference, WCNC 2022
Country/TerritoryUnited States
CityAustin
Period4/10/224/13/22

Keywords

  • Anomaly detection
  • controlled sensing
  • GAN
  • jamming attack
  • reinforcement learning
  • soft actor-critic algorithm

ASJC Scopus subject areas

  • Engineering(all)

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