## 7 Conclusions and Future Work  
This paper has demonstrated that next-generation wireless networks, particularly 5G and the anticipated 6G, require proactive, adaptive, and intelligent security solutions. By proposing a comprehensive security framework integrating machine learning (ML) techniques--from anomaly detection and feature engineering to real-time threat response at the network edge--we have shown how advanced architectures--such as the proposed one--can significantly enhance wireless security. Core to our approach is thus the optimized Wireless Intrusion Detection Algorithm, best represented in the workflow of WIDAT, which reached an overall detection accuracy of approximately 93% against synthetic anomalies. This clearly shows the capability of ML in discovering subtle intrusion patterns in large heterogeneous streams of data; however, WIDA has not yet been benchmarked on live 5G base-station traces, which remains a key objective for future research. Also, we underlined how regulatory recommendations, among which the EU AI Act perhaps is a paradigm, are changing the way such ML-driven solutions are designed and deployed, imposing new constraints and possibilities due to transparency, explainability, and lifecycle management requirements. As wireless networks evolve into critical infrastructures, compliance becomes as important as technical performance, ensuring responsible AI adoption and public trust. In summary, while integrating technologies such as SDN, NFV, and edge computing alongside ML techniques vastly enhances the adaptive security capabilities of next-generation networks, each technology also introduces unique vulnerabilities and complexities. Effective security frameworks must balance these strengths and limitations, leveraging the agility and proactive capabilities of ML and virtualization, while rigorously mitigating potential single points of failure, hypervisor vulnerabilities, resource constraints, and complexity introduced by decentralized architectures. Future research will not only harden ML models against adversarial perturbations but also operationalise the roadmap set out in the latest 6G security survey: deploying quantum-safe cryptography in the physical and connection layers, enforcing zero-trust architectures in the service layer, and embedding policy-driven AI governance throughout the lifecycle. By following this layered, standard-aligned trajectory, we anticipate a measurable reduction in the mean-time-to-detect (MTTD) and mean-time-to-respond (MTTR) across heterogeneous 6G infrastructures.  
Conceptualization, K.K. and C.P.; methodology, K.K. and C.P.; software, K.K. and C.P.; validation, K.K. and C.P.; formal analysis, C.P.; investigation, K.K.; resources, G.F.; data curation, K.K.; writing--original draft preparation, K.K. and C.P.; writing--review and editing, K.K. and C.P.; visualization, C.P.; supervision, G.F.; project administration, K.K.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The test synthetic dataset will be made available by the corresponding author upon reasonable request. No other data were created or analyzed in this study.  
The authors declare no conflicts of interest.