## 6 Machine Learning and 5G Security
### Security Protocol Optimization with Machine Learning  
ML can also be leveraged to optimize the functioning of security protocols in 5G networks. The objective of security protocol optimization is to enhance the network's security performance without excessively consuming computational resources or impacting the network's operational efficiency [127]. One critical area where ML can be utilized is in the optimization of key management protocols, a cornerstone of secure communications in 5G networks. ML algorithms can learn and predict the optimal times for key rotations, thereby increasing the security of encrypted communications while minimizing the overhead associated with the frequent key changes [128]. In addition, machine learning can be used to optimize IDS. Traditional IDS suffer from high false-positive rates, inefficient use of resources, and lack of adaptability. With ML, these systems can be optimized by enhancing their accuracy, improving resource allocation, and enabling them to adapt to evolving threat landscapes. ML can also aid in the optimization of security configurations and policies. Given the complexity of 5G networks, managing and optimizing security settings can be a daunting task. ML algorithms can learn from past incidents, analyze the impact of various configurations, and suggest optimal settings to enhance the security posture of the network [129]. However, it should be noted that the use of ML for security protocol optimization is not without challenges. The effectiveness of ML techniques hinges on the quality and comprehensiveness of the available data. Moreover, the complexity of ML models can sometimes lead to a lack of interpretability, which may pose challenges in understanding and validating the optimized protocols [95]. Table 12 illustrates a comprehensive framework for understanding the role and utility of ML techniques in fortifying 5G network security. The table is structured across multiple dimensions, elaborating on the applications, key techniques, advantages, and challenges of implementing ML in a 5G context. It spans various aspects, from real-time threat detection to predictive security modeling and anomaly identification, encompassing both supervised and unsupervised learning paradigms. The advantages frequently cited include real-time threat mitigation, enhanced network security, and the adaptability to evolving cyber threats. Despite the compelling benefits, the table also highlights key challenges, including data privacy concerns, computational overhead, and issues related to scalability and data quality. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Machine Learning in 5G Security** & **Applications** & **Key Techniques** & **Advantages** & **Challenges** \\ \hline \multirow{6}{*}{
\begin{tabular}{} \end{tabular} } & - & Real-time threat detection & \multirow{6}{*}{-} & \multirow{6}{*}{-} & Real-time threat detection & \multirow{6}{*}{-} \\  & and mitigation & & Anomaly detection & & & \\ \cline{1-1}  & - & Proactive security & - & Predictive modeling & & \\ \cline{1-1}  & - & Large-scale data processing & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Machine learning in 5G security [105; 108; 111; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144]. Further, the supervised and unsupervised learning classification for 5G researchers is summarized in Table 13.  
Table 13 provides an in-depth taxonomy of both supervised and unsupervised learning algorithms that have been employed in the realm of 5G research, according to prominent scholarly publications. The table is particularly useful for academic researchers focusing on machine learning applications in 5G networks. In the domain of supervised learning, the table catalogs a variety of algorithms from Linear Regression to DNNs. Each entry is accompanied by authoritative citations, thus serving as both a summary and a guide for further reading. For example, [PERSON] et al. [147] have contributed to Linear Regression,