Adversarial Jamming Attacks and Defense Strategies via Adaptive Deep Reinforcement Learning

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

Research output: Contribution to journalArticlepeer-review

Abstract

—As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention. In order to address such sensitivity and alleviate the resulting security concerns, we in this paper consider a victim user that performs DRL-based dynamic channel access, and an attacker that executes DRL-based jamming attacks to disrupt the victim. Hence, both the victim and attacker are DRL agents and can interact with each other, retrain their models, and adapt to opponents’ policies. In this setting, we initially develop an adversarial jamming attack policy that aims at minimizing the accuracy of victim’s decision making on dynamic channel access. Subsequently, we devise defense strategies against such an attacker, and propose three defense strategies, namely diversified defense with proportional-integral-derivative (PID) control, diversified defense with an imitation attacker, and defense via orthogonal policies. We design these strategies to maximize the attacked victims accuracy and evaluate their performances.

MSC Codes 68T07 (Primary) 94A15 (Secondary)

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Jul 12 2020

Keywords

  • Adversarial policies
  • Deep reinforcement learning
  • Defense strategies
  • Dynamic channel access
  • Jamming attacks

ASJC Scopus subject areas

  • General

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