Exploring Machine Learning Solutions for Anomaly Detection in 6G Communication Systems

Abstract

The chapter explores the use of machine learning (ML) in detecting and addressing anomalies in advanced 6G communication systems. It emphasizes the drawbacks of conventional approaches and delves into ML algorithms that are appropriate for identifying anomalies, such as clustering, classification, and deep learning. The study focuses on the difficulties of choosing important features from various data sources in 6G networks, including network traffic and device behavior. It also explores possible attacks on ML models and suggests ways to improve their resilience. Exploring integration with network slicing and highlighting the adaptability of ML to dynamic virtualized networks. The chapter highlights the importance of ML-based anomaly detection in strengthening 6G network security and suggests areas for future research.