Waveshield: Machine Learning-Enhanced Physical Layer Security Against Eavesdropping in Wireless Networks

Wireless communication by nature is exposed to eavesdropping because it has an open communicational medium thus traditional encryption cannot be applicable in dynamic environments. In this paper, WaveShield is proposed as a machine-learned physical layer security (PLS) platform, which can be used to secure wireless transmissions against unauthorized interception. WaveShield combines artificial noise injection, beamforming, and randomizing channel state to make the eavesdropper reception worse than, without affecting the legitimate users, maintaining a reliable communication. The framework uses machine learning models to provide prediction of channel variations, optimizing beamforming vectors, and allocating artificial noise dynamically which guarantees proactive defense, unlike the conventional PLS methods. Comprehensive tests of WaveShield on simulated and realworld wireless channel models show that WaveShield is highly effective in enhancing secrecy capacity, bit error rates, and prediction accuracy of dynamic wireless channel channels. Its superiority to conventional PLS approaches is exhibited by comparisons in maintaining secure communication in the face of mobility and low signal to noise ratio conditions. WaveShield offers a framework based on foundations and data, which spans physical layer methods to higher-level security mechanism. It integrates predictive intelligence with adaptive PLS strategies to create a powerful, proactive, and multifaceted system of ensuring that wireless networks in the next generation are secured against serious cases of eavesdropping.