## 2 Architectures of 5G and 6G Networks
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Scope** & **ML Techniques** & **Network Focus** &
\begin{tabular}{c} **Regulatory** \\ **Insight** \\ \end{tabular} & **Distinct Contribution** \\ \hline
[19] & \begin{tabular}{c} Visionary roadmap \\ for 6G: \\ technology enablers \\ \end{tabular} & \begin{tabular}{c} Mentions of AI/ML \\ in edge/cloud \\ operations and \\ transceiver design \\ \end{tabular} & Primarily 6G (vision-level) & \
\begin{tabular}{c} High-level policy \\ drivers (UN SDGs, \\ privacy, \\ spectrum ethics) \\ \end{tabular} &
\begin{tabular}{c} Sets a broad research \\ agenda; no specific focus \\ on ML-based security \\ or IDS \\ \end{tabular} \\ \hline
[22] & \begin{tabular}{c} Security survey \\ covering both \\ physical and \\ logical layers \\ \end{tabular} & \begin{tabular}{c} Explores ML in \\ anomaly detection, \\ access control, \\ adversarial attacks \\ \end{tabular} & Focused on 6G & \begin{tabular}{c} Addresses \\ post-quantum \\ crypto, ethical AI, \\ GDPR, and \\ blockchain trust \\ \end{tabular} & Reviews holistic security & but not specialized in \\ \hline This & \begin{tabular}{c} Proposed framework \\ and systematic \\ review of ML-based \\ intrusion detection \\ in 5G/6G networks \\ \end{tabular} & \begin{tabular}{c} Supervised, \\ unsupervised, deep \\ learning, \\ reinforcement \\ learning, federated \\ learning \\ \end{tabular} & \begin{tabular}{c} Supervised, \\ unsupervised, deep \\ learning, \\ and emerging 6G \\ \end{tabular} & \
\begin{tabular}{c} Detailed \\ comparison with \\ Article 5 of the EU \\ AI Act, \\ zero-trust APIs \\ \end{tabular} &
\begin{tabular}{c} Unique in merging \\ ML-IDPS taxonomy, \\ adaptive ML-based \\ security framework for \\ 5G/6G networks, \\ real-world datasets, \\ Unique WIDA algorithm \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative summary of related works. * **Blockchain-Anchored Model Provenance:** Leverages blockchain to secure ML model checkpoints, providing tamper-evident audit trails for every intrusion detection model update. * **Self-Adaptive Model Fine-Tuning:** Enables on-device model retraining triggered by drift thresholds, ensuring resilience against evolving attack vectors. Therefore, safeguarding these dynamic environments requires adaptive security frameworks--such as the proposed one--leveraging ML-based intrusion detection and prevention systems. Such systems can learn from traffic patterns, identify anomalies, and autonomously reconfigure network defenses to counter evolving threats. Rigorous security testing, certification of SDN controllers and hypervisors, robust edge security protocols, and continuous monitoring of virtualized network slices will be critical [22]. Therefore, the transformation from 4G to 5G--and eventually to 6G--is more than just a technological upgrade; it constitutes a reimagining of the fundamental networking paradigm. While soft-warization, virtualization, and edge-centric computing have greatly improved performance and flexibility, they also expose new security vulnerabilities that lend more importance to real-time monitoring, automated threat response, and strong governance frameworks. Machine Learning Techniques and Comparative Overview Against Established Intrusion Detection Methods  
ML provides a feasible approach to the detection of complex intrusions and anomalies in wireless networks. ML, with its high-tech algorithms and feature engineering techniques, can improve the capability of IDS to detect and handle complex security problems effectively.