Cooperative Proactive Eavesdropping Based on Deep Reinforcement Learning

• Published in: IEEE wireless communications letters Vol. 10; no. 9; pp. 1857 - 1861
• Main Authors: Yang, Yaxin, Li, Baogang, Zhang, Shue, Zhao, Wei, Zhang, Haijun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; cooperative jamming; Cooperative proactive eavesdropping; Decision making; Eavesdropping; eavesdropping energy efficiency; Finite capacity; Heuristic algorithms; Jamming; Links; Machine learning; MADDPG; Multiagent systems; Receiving antennas; Signal to noise ratio; Wireless communication
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2021.3084213

There is illegitimate transmission of information between suspicious users, whereas single legitimate monitor (LM) has finite capacity to satisfy eavesdropping. This letter studies a cooperative proactive eavesdropping(CPE) scheme, where two LMs eavesdrop on multiple suspicious links simultaneously with a cooperative jamming method under finite power constraint. Specifically, two LMs collaborate to emit jamming signals for the purpose of influencing the rate of suspicious links and facilitate successful eavesdropping at each LM. However, how to make jamming power decision over multiple suspicious links to maximize cumulative sum eavesdropping energy efficiency (EEE) in a long term for each LM under dynamic environment and continuous action space is a huge challenge. To solve the dynamic decision-making problem, we use multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve the cooperative jamming problem. In simulation, the results show that our proposed CPE scheme can obtain the effective sum EEE compared with DDPG-based scheme and Random policy scheme.