{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:37:50Z","timestamp":1782967070789,"version":"3.54.5"},"reference-count":63,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon Europe Research and Innovation Programme under the Marie Sk\u0142odowska-Curie Postdoctoral Fellowship Programme","award":["101081355"],"award-info":[{"award-number":["101081355"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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 (\u03b1-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 \u03b1-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 10\u22123 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.<\/jats:p>","DOI":"10.3390\/e27080815","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T16:16:10Z","timestamp":1753805770000},"page":"815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning-Assisted Secure Random Communication System"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7011-4968","authenticated-orcid":false,"given":"Areeb","family":"Ahmed","sequence":"first","affiliation":[{"name":"University of Ljubljana, Faculty of Computer and Information Science, Ve\u010dna pot 113, 1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0501-7146","authenticated-orcid":false,"given":"Zoran","family":"Bosni\u0107","sequence":"additional","affiliation":[{"name":"University of Ljubljana, Faculty of Computer and Information Science, Ve\u010dna pot 113, 1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107372","DOI":"10.1016\/j.compeleceng.2021.107372","article-title":"A survey on 5G\/6G, AI, and robotics","volume":"95","author":"Qiao","year":"2021","journal-title":"Comput. 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