## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security
### AI, ML and Network Security  
The challenges in managing telecommunications networks have increased due to service offerings' growth and network settings' escalating complexity [52; 64]. AI has emerged as a viable option to meet the demands of the new paradigm of networks, as varied services with varying requirements must be handled efficiently [65]. In this context, AI is a crucial automation tool, simplifying a range of network operations, including design, deployment, monitoring, configuration changes, planning, problem detection, and security enhancement [48]. AI can help with growth, intelligent planning and strategy, data retrieval, and autonomous network modification [66]. Thus, it is expected that AI will allow advanced security measures to be implemented, protecting sensitive data and network integrity from constantly evolving cyberthreats. As previously mentioned, integrating AI is a significant option in this constantly changing environment, where a range of services need to run inside slices while maintaining strong security measures. The dynamic and ever-changing nature of the threat landscape highlights the importance of deploying cutting-edge systems for threat detection and incident response [67]. Using AI to enhance security in network slicing is paramount due to its multifaceted capabilities [68]. AI is thought to tackle several current issues, mainly when powered by ML algorithms. It gives machines the ability to think, reason, anticipate, make decisions, and behave intelligently--similar to human capacities [65]. This capability makes real-time resource optimization and proactive problem-solving possible. AI can provide security through several techniques, including anomaly detection, intrusion detection, and quickly resolving possible threats [69]. Large volumes of data, including network traffic, user actions, and device interactions, are analysed by these systems in real-time to spot anomalies and possible security breaches. AI-driven intrusion detection systems can sound alarms and launch quick reactions to efficiently neutralise threats by continually scanning for suspicious activity and new attack patterns [67]. AI's capacity to adapt and learn from historical data, which enables it to detect known and unknown threats accurately, is one of its main advantages in increasing security. It is effective at spotting suspicious behaviour patterns, even when they diverge from established dangers, making the network more flexible [69]. Predictive analytics powered by AI also helps operators foresee security breaches by seeing new risks before they become serious [70]. The network's adaptive capabilities are strengthened by this proactive strategy, which enables it to keep up with changing threats and uphold robust security protocols [71; 72]. Using AI also benefits from facing more automated and sophisticated attacks [22]. Overall, AI significantly enhances the security of 5G networks by providing intelligent threat detection, adaptive defences, and rapid response capabilities. Its ability to analyse vast amounts of data, adapt to evolving threats, and anticipate security breaches makes it an indispensable tool for safeguarding critical assets and ensuring the integrity of telecommunications networks in the face of increasingly sophisticated cyber threats. Challenges Posed by AI/ML  
Despite becoming the most popular method for improving network slicing security, it has a few drawbacks. First, to produce intelligent actions for classification or prediction, ML algorithms rely on the collection and processing of data for training. Malicious actors, conversely, can take advantage of security flaws by executing adversarial attacks on ML systems, such as injecting fictitious datasets for training or altering transmitted data, leading to incorrect results [65]. The nature of ML also has drawbacks, such as a high false alarm rate (false positives) or even false negatives when the algorithm fails to identify a threat and the need for a lot of resources to train and maintain these algorithms.