Machine learning-assisted secure random communication system

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

Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random communication system (ML-RCS). It comprises a pretrained decision tree (DT)-based receiver that extracts binary information from the transmitted random noise carrier signals. The ML-RCS employs skewed alpha-stable ($\alpha$-stable) noise as a random carrier to encode the incoming binary bits securely. The DT model is pretrained on an extensively developed dataset encompassing all the selected parameter combinations to generate and detect the $\alpha$-stable noise signals. The legitimate receiver leverages the pretrained DT and a predetermined key, specifically the pulse length of a single binary information bit, to securely decode the hidden binary bits. The performance evaluations included the single-bit transmission, confusion matrices, and a bit error rate (BER) analysis via Monte Carlo simulations. The fact that the BER reached $y=10^{-3}$ confirms the ability of the proposed system to establish successful secure communication between a transmitter and legitimate receiver. Additionally, the ML-RCS provides an increased data rate compared to previous random communication systems. From the perspective of security, the confusion matrices and computed false negative rate of 50.2% demonstrate the failure of an eavesdropper to decode the binary bits without access to the predetermined key and the private dataset. These findings highlight the potential ability of unconventional ML-RCSs to promote the development of secure next-generation communication devices with built-in PLSs.

• Language: English
• Keywords: machine learning, decision tree, 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: 22 str.
• Numbering: Vol. 27, iss. 8, art. 815
• PID: 20.500.12556/RUL-175499
• UDC: 004.85:004.056:621.39
• ISSN on article: 1099-4300
• DOI: 10.3390/e27080815
• COBISS.SI-ID: 244282627
• Publication date in RUL: 29.10.2025

Record is a part of a journal

• Title: Entropy
• Shortened title: Entropy
• Publisher: MDPI
• ISSN: 1099-4300
• COBISS.SI-ID: 515806233

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, odločitveno drevo, prikrita komunikacija, naključni komunikacijski sistem

Projects

• Funder: EC - European Commission
• Funding programme: HE
• Project number: 101081355
• Name: Machine learning for Sciences and Humanities
• Acronym: SMASH