Machine Learning for Enabling High-Data-Rate Secure Random Communication: SVM as the Optimal Choice over Others

Areeb Ahmed, Zoran Bosnić

Machine learning (ML) has become a key ingredient in revolutionizing the physical layer security of next-generation devices across Industry 4.0, healthcare, and communication networks. Many conventional and unconventional communication architectures now incorporate ML algorithms for performance and security enhancement. In this study, we propose an unconventional, high-data-rate, machine-learning-driven, secure random communication system (HDR-MLRCS). Instead of utilizing traditional static methods to encrypt and decrypt alpha-stable (α-stable) noise as a random carrier, we integrated several ML algorithms to convey binary information to the intended receivers covertly. A support vector machine-aided receiver (SVM-R), Naïve Bayes-aided receiver (NB-R), k-Nearest Neighbor-aided receiver (kNN-R), and decision tree-aided receiver (DT-R) were integrated into a single architecture to provide an accelerated data rate with robust security. All intended receivers were pre-trained on a restricted-access dataset (R-