Jamming Pattern Recognition over Multi-Channel Networks: A Deep Learning Approach

TL;DR

This paper presents a deep learning-based method for recognizing jamming patterns in multi-channel wireless networks, enabling real-time detection and adaptive countermeasures against intelligent, policy-changing jammers.

Contribution

It introduces a recurrent neural network approach for real-time jammer type recognition, allowing dynamic anti-jamming responses to sophisticated, adaptive threats.

Findings

Detection accuracy exceeds 70% with 5-slot policy switching

Accuracy improves to 90% with 45-slot switching

The method enables rapid adaptation to changing jamming strategies

Abstract

With the advent of intelligent jammers, jamming attacks have become a more severe threat to the performance of wireless systems. An intelligent jammer is able to change its policy to minimize the probability of being traced by legitimate nodes. Thus, an anti-jamming mechanism capable of constantly adjusting to the jamming policy is required to combat such a jammer. Remarkably, existing anti-jamming methods are not applicable here because they mainly focus on mitigating jamming attacks with an invariant jamming policy, and they rarely consider an intelligent jammer as an adversary. Therefore, in this paper, to employ a jamming type recognition technique working alongside an anti-jamming technique is proposed. The proposed recognition method employs a recurrent neural network that takes the jammer's occupied channels as inputs and outputs the jammer type. Under this scheme, the real-time jammer policy is first identified, and, then, the most appropriate countermeasure is chosen. Consequently, any changes to the jammer policy can be instantly detected with the proposed recognition technique allowing for a rapid switch to a new anti-jamming method fitted to the new jamming policy. To evaluate the performance of the proposed recognition method, the accuracy of the detection is derived as a function of the jammer policy switching time. Simulation results show the detection accuracy for all the considered users numbers is greater than 70% when the jammer switches its policy every 5 time slots and the accuracy raises to 90% when the jammer policy switching time is 45.