## 6 Machine Learning and 5G Security
### The Role of Machine Learning in Enhancing 5G Security  
The use of ML algorithms offers an advanced and effective solution to enhance and reduce the security challenges associated with the advent of 5G networks [7; 8]. The inherent attributes of ML, such as adaptability, prediction capability, and large-scale data processing, provide promising solutions to handle the unprecedented scale and complexity of the 5G ecosystem. The onset of 5G networks has brought about a paradigm shift in the world of telecommunications, promising high-speed connectivity, low latency, and a seamless integration of billions of devices across the globe. However, this immense progress also brings with it a range of novel security challenges that require innovative solutions. ML algorithms can help to develop intelligent 5G security systems capable of self-learning and adapting to evolving threats, allowing for real-time threat detection and mitigation [7; 39]. Furthermore, ML algorithms are capable of processing massive volumes of data generated by 5G networks, extracting valuable insights about possible threats and providing proactive security measures to prevent potential attacks [109]. ML has emerged as a potent tool in this context, offering robust capabilities to improve the security framework in 5G networks. Machine learning, with its capabilities for pattern recognition, anomaly detection, and predictive modeling, plays a pivotal role in strengthening the security apparatus of 5G networks [39; 109]. The implementation of ML in 5G security is thereby poised to significantly improve the robustness and reliability of the 5G infrastructure, contributing to the overall sustainability of the digital ecosystem in the era of 5G and beyond. Generally, the role of ML in 5G security is multifaceted, spanning areas like intrusion detection, privacy preservation, secure routing, and threat intelligence, among others. It holds the potential to transform the conventional, reactive security frameworks into proactive, intelligent systems capable of thwarting cyberattacks before they can inflict significant damage [110]. The potential of ML in the realm of 5G security enhancement is significant. As 5G networks evolve into dynamic and complex systems, traditional security measures struggle to effectively manage the extensive network topology, heterogeneous traffic patterns, and multi-dimensional data. Machine learning is particularly adept at handling these challenges, as its capabilities extend beyond mere rule-based systems. In 5G networks, ML algorithms can intelligently process vast amounts of network data, promptly recognizing potential security threats through anomaly detection [111]. The capability of ML to learn from past data and experiences allows it to accurately distinguish normal network behavior from malicious activities, providing effective real-time threat detection. Moreover, through predictive analytics, ML can foresee potential attacks, giving network operators valuable lead time to mitigate potential damages. Another key strength of ML is its adaptability, which enables it to learn from new situations and update its predictive models accordingly. This continuous learning feature is vital for coping with the evolving nature of cyber threats in 5G networks. Moreover, ML can improve resource allocation and optimize security protocols, enhancing both network security and performance [112]. A myriad of machine learning techniques holds substantial relevance in the realm of 5G security, offering unique capabilities for different aspects of network security management [113]. Supervised learning techniques such as support vector machines (SVMs)and decision trees are commonly used for intrusion detection in 5G networks [114]. These algorithms learn from labeled training data to classify network activities as either normal or malicious [115]. In particular, decision trees are beneficial for their ease of interpretation, enabling network administrators to understand the decision-making process behind the detection of potential threats.