# Machine Learning-Assisted Secure Random Communication System
## 2 Machine Learning for Physical Layer Security  
ML has recently evolved into a potential candidate for fortifying PLS and achieving low-latency communication through an open radio access network architecture. It offers adaptive techniques that are based on a data-driven approach to resisting incoming threats and dynamically adjusting the defenses of 5G and 6G systems [18]. The contributions of machine learning to strengthening PLS can be categorized into three domains:  
1. User and device authentication via ML. 2. Intrusion detection systems via ML. 3. Confidentiality using ML. 1. In the case of authentication, ML techniques, such as decision trees (DTs), support vector machines (SVMs), and k-nearest neighbors (KNNs), exploit physical channel features, such as channel state information and time of arrival, to achieve reliable results for MIMO systems and mobile scenarios [19]. Channel analysis [20] and signal features [21] can also help to determine the class in order to identify or detect potential attacks. This shows the versatility of ML for elevating PLS. 2. For intrusion detection, ML classifiers, such as SVMs, KNNs, DTs, and other boosting algorithms, have already been employed to perform attack identification in wireless systems, automation systems, and IoT networks [22; 23; 24]. Advanced techniques, including autoencoders [25] and graph neural networks [26], have also utilized temporal and multivariate features for intrusion detection. Moreover, versatile approaches have been adopted in order to identify unknown attacks on communication systems; these include simultaneous classification in address jamming [27] and incorporating perimeter intrusion detection with the generation of a curated dataset [28]. 3. Specifically, to ensure confidentiality between a transmitter and legitimate receiver, the channel state information (CSI) has been exploited as a secure key in various studies. For example, eavesdropping attempts have been rebuffed by the use of secure beamforming vectors adapted to the CSI, in such a way that the original data can be recovered only by legitimate users [29]. Moreover, machine learning-based transmitter antenna selection with partial CSI (the CSI of an eavesdropper is unknown) and full CSI (the CSI of an eavesdropper is known) has been explored by deploying an SVM and naive Bayes to maximize channel secrecy [30]. Overall, ML has contributed significantly to the enhancement of PLS in all the categories discussed above. Specifically, DT algorithms play a key role in the PLS enhancement of communication systems. They provide a versatile method for intrusion detection, channel authentication, and confidentiality. As ML algorithms, they have been used extensively in the development of underlying models that focus on data security and privacy [31]. Owing to the low computational complexity of DTs, it is widely recognized that they ensure secure communication across diverse fields, including biomedicine, mobile healthcare, power systems, automated systems, cloud computing, wireless sensor networks, and cybersecurity [32; 33; 34; 35; 36; 37; 38]. Less complex DTs are now being specifically designed in order to optimize the privacy and security of communication systems [39; 40]. Therefore, compared with traditional static approaches, they are now considered more effective for addressing the unique challenges of PLS in communication systems. However, well-known ML algorithms, such as neural networks, naive Bayes, logistic regressions, random forests, support vector machines, decision trees, and k-nearest neighbors, have significantly improved accuracy, recall, and precision across different training datasets. However, the performance of ML algorithms incorporated within conventional communication systems begins to diminish after a certain security benchmark is achieved [41]. This stresses the vulnerabilities of ML approaches when they are incorporated into conventional communication systems.