# Machine Learning-Assisted Secure Random Communication System
## 2 Machine Learning for Physical Layer Security
Moreover, it is necessary toexplore the possibility of incorporating ML into unconventional communication systems to enhance the PLS. Among the various unconventional communication mechanisms, the utilization of noise as a random carrier can be integrated into ML algorithms. Specifically, if secure communication must be established in the additive white Gaussian noise (AWGN) channel, alpha-stable (\(\alpha\)-stable) noise can provide coverage by exploiting its non-Gaussian distribution characteristics. In general, it has been used to model channels that exhibit heavy tails, skewed data, and responses that are vulnerable to unpredictable fluctuations. However, its ability to covertly convey binary information to legitimate receivers has also been exploited. The key advantage of utilizing \(\alpha\)-stable noise as a carrier is twofold: first, it safeguards encrypted binary information, and second, it masks the presence of communication [42]. Random communication systems (RCSs) have initiated the utilization of symmetric \(\alpha\)-stable (S\(\alpha\)S) and skewed \(\alpha\)-stable (SkaS) noise as random carrier signals to establish covert communication between a transmitter and legitimate receiver [43; 44]. Both schemes utilize static estimation methods, such as the sinc estimator and logarithmic estimator, to decrypt the transmitted random carrier signals at the legitimate receiver by utilizing a predetermined pulse length. The most refined design was later proposed on the basis of the maximum extreme value method-based estimator [45]. This provided an optimized version of an RCS with respect to the bit error rate. This was followed by the development of the first synchronization method for RCSs, paving the way for their practical implementation in real-world applications, such as military communications, vehicular networks, the internet of things, industrial control systems, and satellite networks [46]. Studies have also been conducted to expand the symbol capacity by introducing multiple-level M-ary modulation schemes [47]. This resulted in an increase in the data rate achievable by RCSs. The PLS achievable by RCSs is theoretically guaranteed due to the nonexistence of higher-order statistical properties in \(\alpha\)-stable noise. This results in an inherent resistance to eavesdropping as a random carrier. Therefore, the initially proposed architectures focused on improving the bit error rate (BER). Nevertheless, several RCS architectures concatenated with other entities, such as inverse systems and signal multiplexers, were later proposed [48; 49]. The primary objective of these studies was to extend the parameters or private keys from a single parameter, that is, the pulse length, to multiple parameters. This was because these parameters or private keys govern the coverage of RCSs. The approach of deploying various static estimators along with the integration of several entities in an RCS has improved the performance of the BER and security [50]. On the other hand, the practicality of RCSs for the internet of things has also been tested, which has strengthened its potential for real world applications [51]. However, there is still a significant gap between conventional communication systems and \(\alpha\)-stable noise-based unconventional RCSs in terms of the data rate scale. ML algorithms could play a key role in reducing this gap. This could further prove their applicability for strengthening the PLS of future 6G nano-communication systems if they can compete with conventional communication in terms of the data rate [52]. Considering these factors, we propose a novel machine learning-assisted secure random communication system (ML-RCS). This is the first attempt to deploy any ML algorithm to encrypt and decrypt the \(\alpha\)-stable noise at a transmitter and legitimate receiver, respectively. The proposed method belongs to the category of ML-based user and device authentications. The proposed ML-RCS establishes covert communication in the AWGN channel and strengthens the PLS of the system.