Machine learning-assisted secure random communication system

Ahmed, Areeb (avtor), Bosnić, Zoran (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: machine learning, decision tree, covert communication, random communication system
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 22 str.
• Številčenje: Vol. 27, iss. 8, art. 815
• PID: 20.500.12556/RUL-175499
• UDK: 004.85:004.056:621.39
• ISSN pri članku: 1099-4300
• DOI: 10.3390/e27080815
• COBISS.SI-ID: 244282627
• Datum objave v RUL: 29.10.2025

Gradivo je del revije

• Naslov: Entropy
• Skrajšan naslov: Entropy
• Založnik: MDPI
• ISSN: 1099-4300
• COBISS.SI-ID: 515806233

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, odločitveno drevo, prikrita komunikacija, naključni komunikacijski sistem

Projekti

• Financer: EC - European Commission
• Program financ.: HE
• Številka projekta: 101081355
• Naslov: Machine learning for Sciences and Humanities
• Akronim: SMASH