20.500.12556/RUL-176018 Machine learning for enabling high-data-rate secure random communication SVM as the optimal choice over others 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-D) and exploited a static key—the pulse length—to generate and successfully classify α-stable noise samples to extract hidden binary digits. We demonstrated the performance of the proposed HDR-MLRCS by simulating 4-bit and 1000-bit transmissions (including bit error rates and confusion matrices) from the perspectives of the intended receivers and the eavesdropper receiver (E-R). The significance of the HDR-MLRCS lies in its significantly higher data rates compared to previously proposed counterparts using static receivers. At the same time, the SVM-R consistently outperformed all other considered intended receivers. Moreover, the decisive failure of E-R ensures the architecture’s resistance to possible interception of communications. The fusion of high data throughput and robustness, enabled by the utilization of ML and α-stable noise as a random carrier, highlights the suitability of HDR-MLRCS for future secure communication infrastructures. machine learning covert communication random communication system strojno učenje skrita komunikacija naključni komunikacijski sistem true false true Angleški jezik Slovenski jezik Članek v reviji 2025-11-18 13:01:14 2025-11-18 13:01:15 2025-11-19 04:31:53 0000-00-00 00:00:00 2025 0 0 Str. 1-22 iss. 22, art. 3590 Vol. 13 Nov. 2025 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.85:621.39 2227-7390 10.3390/math13223590 256493571 RAZ_Ahmed_Areeb_2025.pdf RAZ_Ahmed_Areeb_2025.pdf 1 C327D496CE490A99DAC9D320C72A3BFC 7f4a626bf4cf002e514e1041aea5cdb1c955836b5cf9628afad9ac6a4f331ac5 9a3acfee-c476-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=222060 https://www.mdpi.com/2227-7390/13/22/3590 1 f0ab7888-c475-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=222059 Fakulteta za računalništvo in informatiko 0 0 0