How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning

2025-10-02

AI Summary

Dynamic reactive jamming attacks—where jammers sense the environment in real time and adaptively select channels and detection thresholds to disrupt communications—pose significant challenges to cognitive radio networks. Method: This paper proposes a hybrid deep reinforcement learning (DRL) framework for anti-jamming cognitive radios, integrating Q-learning (for discrete jamming events) with Deep Q-Networks (to model continuous states such as received power), jointly optimizing transmit power, modulation scheme, and channel selection under unknown channel dynamics and jamming strategies. Contributions/Results: First, it introduces a dual-granularity state representation within a unified DRL architecture—the first of its kind. Second, it designs a multi-objective reward function balancing throughput, energy efficiency, and robustness. Experiments demonstrate rapid convergence under rapidly evolving jamming policies, a 23.6% improvement in long-term throughput, and superior spectral efficiency and anti-jamming robustness compared to baseline methods.

Technology Category

• Intelligent Robots: Cognitive Robotics
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmissions. The transmitter-receiver pair learns to avoid jamming and optimize throughput over time (without prior knowledge of channel conditions or jamming strategies) by using reinforcement learning (RL) to adapt transmit power, modulation, and channel selection. Q-learning is employed for discrete jamming-event states, while Deep Q-Networks (DQN) are employed for continuous states based on received power. Through different reward functions and action sets, the results show that RL can adapt rapidly to spectrum dynamics and sustain high rates as channels and jamming policies change over time.

Problem

• Mitigating reactive jamming with dynamic channel selection
• Optimizing throughput using reinforcement learning without prior knowledge
• Adapting transmission parameters to counter evolving jamming strategies

Innovation

• Uses reinforcement learning to adapt transmission parameters
• Employs Q-learning for discrete and DQN for continuous states
• Dynamically adjusts power, modulation, and channel selection