A Deep Neural Network Assisted Physical Layer Security Mechanism for Wireless Networks

The physical layer in wireless systems is inherently susceptible to eavesdropping, jamming, spoofing, and signal injection attacks because wireless signals propagate through open space. Unlike wired networks, where physical access is more controlled, wireless communication can be intercepted by any nearby device. Attackers can exploit features such as channel reciprocity, power control, or modulation characteristics to compromise communications. These vulnerabilities make it imperative to secure the physical layer, especially in applications involving sensitive data like military, healthcare, and IoT-based services, which rely primarily on wireless data transmission. This paper presents a machine learning assisted security aware channel assignment protocol against possible adversarial eavesdropping attacks. The ML parameters such as gradient, iterations to convergence and cost function have been computed and presented. The final error rate with and without the proposed system under adversarial attacks is also presented. It can be observed that the proposed approach is close to the no adversarial attack condition clearly indicating the efficacy of the approach to proactively thwart potential attacks. The error rates are also significantly lower than existing approach in the domain. Additionally, to enhance the QoS of the system, a handoff mechanism is also proposed in conjugation with equalization. The metrics for evaluation of the performance of the system are the BER, scatter and MSE.