ML-Based Anomaly Detection in 6G Networks: A Survey on the Current Status, Challenges, and Future Directions

As the development of 6G networks accelerates, ensuring robust security measures becomes paramount to safeguard against emerging threats. Anomaly detection, a crucial aspect of cybersecurity, plays a pivotal role in identifying deviations from normal network behavior indicative of potential security breaches. Thus, this paper presents a comprehensive survey of anomaly detection techniques leveraging machine learning in the context of 6G networks and explores machine learning algorithms, datasets, evaluation metrics, and challenges associated with anomaly detection in 6G networks. This leads to a summary of potential future research directions, which can enhance the efficacy of anomaly detection mechanisms in securing next-generation networks.