# Machine Learning-Assisted Secure Random Communication System
## 1 Introduction  
Machine learning (ML) has transformed all aspects of contemporary communication systems, such as signal processing, channel estimation, resource allocation, error correction, and security protocols. This has enhanced the efficiency, flexibility, and reliability of the current 5G and next-generation 6G systems [1, 2]. Specifically, regarding signal detection and processing, model-driven deep learning networks have enhanced the performance of various channel environments for multiple-input multiple-output (MIMO) detection by optimizing the trainable parameter errors [3]. Similarly, within channel estimation, prediction, and compression, new ML approaches that are capable of adapting to changing channel conditions through online training and occasional retraining have been presented for multicarrier systems [4]. ML has also aided in the active resource management ofthe previous 5G spectrum and more advanced networks for radio access network (RAN) slicing [5]. In addition, the application of machine learning with a rateless scheme for error correction and decoding has established future trends in wireless communications [6]. Moreover,, the integration of ML with conventional protocols in order to optimize every component of communication systems has been extensively explored. Additionally, its ability to enhance security in preliminary open system interconnection (OSI) layers is also attracting attention with regard to future-generation security schemes. In the above context of OSI layers, the physical layer security (PLS) is considered the front wall of any communication system. This enhances confidentiality without relying solely on cryptographic methods. It prevents unauthorized access to the first layer by leveraging the unique characteristics of the communication channels [7]. Boosting the PLS of all communication systems is the most critical requirement of the current global digital era. Recent technological advancements, new challenges, innovative solutions, and the prospects for PLS techniques with regard to the present 5G systems and upcoming 6G networks have gained considerable attention [8; 9]. The security of 5G access networks has evolved over the decades, and researchers are now dedicated to the development of physical-level security architectures from scratch. This includes the use of various techniques, such as millimeter-wave communication, PLS coding, nonorthogonal multiple access, massive MIMO, and other advanced and conventional methods. It is also necessary to meet the needs of the main 6G use cases, including autonomous cyber-physical systems, machine communications, and ultralow-latency communications [10; 11]. PLS has been identified as a potential solution to the underlying problems associated with traditional encryption methods, but it is considered to have several limitations [12]. As a result, its usage has diversified into other areas, such as military communications, vehicular networks, the internet of things (IoT), industrial control systems, and satellite networks [13; 14; 15; 16; 17]. These developments call for the creation of new PLS mechanisms and the assessment of previous mechanisms from different angles. This paper contributes to the existing literature in the following ways:  
* Incorporation of ML into RCSs: This paper contributes to PLS by proposing the first possible configuration of a machine learning-assisted random communication system (ML-RCS). * Enhanced data rate and security: Our proposed user and device authentication model shows increased performance and security between a transmitter and legitimate receiver. It strengthens the PLS by covertly conveying the binary information by utilizing noise as the carrier signals. Moreover, the incorporation of an ML algorithm provides increased data rates in comparison to the previously proposed models. * Baseline model and benchmark for comparison: The paper can be considered as presenting a baseline model for encrypting and decrypting \(\alpha\)-stable noise signals by utilizing ML algorithms.