Machine learning for enabling high-data-rate secure random communication : SVM as the optimal choice over others

Ahmed, Areeb (author), Bosnić, Zoran (author)

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.

• Language: English
• Keywords: machine learning, covert communication, random communication system
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: Str. 1-22
• Numbering: Vol. 13, iss. 22, art. 3590
• PID: 20.500.12556/RUL-176018
• UDC: 004.85:621.39
• ISSN on article: 2227-7390
• DOI: 10.3390/math13223590
• COBISS.SI-ID: 256493571
• Publication date in RUL: 18.11.2025

Record is a part of a journal

• Title: Mathematics
• Shortened title: Mathematics
• Publisher: MDPI AG
• ISSN: 2227-7390
• COBISS.SI-ID: 523267865

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: strojno učenje, skrita komunikacija, naključni komunikacijski sistem