Configuring KNN-Based Receiver for Machine Learning-Assisted Secure Random Communication System Under Gaussian Environment

Physical Layer Security (PLS) is considered as the boundary wall of modern communication systems. In this study, we investigated the possibility of ensuring PLS through unconventional random communication systems by incorporating a supervised machine learning algorithm. The proposed machine learning-assisted secure random communication system (ML-RCS) exploits skewed alpha-stable ($\alpha$-stable) noise signals as random carriers for secure transmission of binary digits. At the authorized reception side, we utilize a KNN-based receiver, who has been pretrained on a private $\boldsymbol{\alpha}$-stable noise dataset (Private- $\boldsymbol{\alpha}$ SND) which consists of privately chosen combinations of parameters required to encrypt and decrypt the transmitted $\alpha$-stable noise signals. Along with the secure key-based dataset, a static key (the pulse length) is also needed for exact decryption of hidden binary digits. We evaluated the approach using bit error rate (BERs) plots with all possible parameter's combinations present in Private- $\alpha$ SND by performing Monte Carlo simulations. With this approach, we practically evaluated the behavior of MLRCS under additive white gaussian noise (AWGN) channel. The results show that the ML-RCS is resistant to eavesdropping under all AWGN channel environments, which makes it a prospective candidate to establish secure communication by unconventional means.