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

TL;DR

This paper introduces a deep reinforcement learning approach using deep Q-networks to effectively counter proactive jamming attacks in resource-constrained IoT networks, outperforming traditional methods.

Contribution

It develops and evaluates multiple DQN variants tailored for IoT devices, demonstrating a robust, sample-efficient anti-jamming solution without needing jamming pattern detection.

Findings

DRL approach effectively counters various jamming strategies

Proposed agents are lightweight and suitable for power-constrained IoT devices

Simulation results show significant improvement over traditional techniques

Abstract

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.