Defeating Proactive Jammers Using Deep Reinforcement Learning for Resource-Constrained IoT Networks

Traditional anti-jamming techniques like spread spectrum, adaptive power/rate control, and cognitive radio, have demonstrated effectiveness in mitigating jamming attacks. However, their robustness against the growing complexity of internet-of-thing (IoT) networks and diverse jamming attacks is still limited. To address these challenges, machine learning (ML)-based techniques have emerged as promising solutions. By offering adaptive and intelligent anti-jamming capabilities, ML-based approaches can effectively adapt to dynamic attack scenarios and overcome the limitations of traditional methods. In this paper, we propose a deep reinforcement learning (DRL)-based approach that utilizes state input from realistic wireless network interface cards. We train five different variants of deep Q-network (DQN) agents to mitigate the effects of jamming with the aim of identifying the most sample-efficient, lightweight, robust, and least complex agent that is tailored for power-constrained devices. The simulation results demonstrate the effectiveness of the proposed DRL-based anti-jamming approach against proactive jammers, regardless of their jamming strategy which eliminates the need for a pattern recognition or jamming strategy detection step. Our findings present a promising solution for securing IoT networks against jamming attacks and highlights substantial opportunities for continued investigation and advancement within this field

Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLF-PHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.Comment: This paper appears in the Proceedings of IEEE Global Communications Conference (GLOBECOM) 2020. A full version appears in IEEE Transactions on Wireless Communications. arXiv:2004.1253