DeepFake: Deep Dueling-Based Deception Strategy to Defeat Reactive Jammers

Van Huynh, Nguyen; Hoang, Dinh Thai; Nguyen, Diep N.; Dutkiewicz, Eryk

Abstract

In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks. In particular, for a smart and reactive jamming attack, the jammer is able to sense the channel and attack the channel if it detects communications from the legitimate transmitter. To deal with such attacks, we propose an intelligent deception strategy which allows the legitimate transmitter to transmit “fake” signals to attract the jammer. Then, if the jammer attacks the channel, the transmitter can leverage the strong jamming signals to transmit data by using ambient backscatter communication technology or harvest energy from the strong jamming signals for future use. By doing so, we can not only undermine the attack ability of the jammer, but also utilize jamming signals to improve the system performance. To effectively learn from and adapt to the dynamic and uncertainty of jamming attacks, we develop a novel deep reinforcement learning algorithm using the deep dueling neural network architecture to obtain the optimal policy with thousand times faster than those of the conventional reinforcement algorithms. Extensive simulation results reveal that our proposed DeepFake framework is superior to other anti-jamming strategies in terms of throughput, packet loss, and learning rate.

• Journal Article Type: Article
• Acceptance Date: May 5, 2021
• Online Publication Date: May 17, 2021
• Publication Date: 2021-10
• Deposit Date: Mar 29, 2023
• Journal: IEEE Transactions on Wireless Communications
• Print ISSN: 1536-1276
• Electronic ISSN: 1558-2248
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 10
• Pages: 6898-6914
• DOI: https://doi.org/10.1109/twc.2021.3078439
• Keywords: Anti-jamming, reactive jammer, deception mechanism, ambient backscatter, RF energy harvesting, deep dueling, deep Q-learning, deep reinforcement learning