The evolution of network technologies including the rise of 5G, 6G, and the Internet Of Things (IoT) is fundamentally transforming the landscape of digital connectivity. These new-generation networks introduce unprecedented levels of complexity, distribution, and dynamism, making the detection of anomalies a critical challenge for ensuring security, reliability, and performance.
This report provides a state-of-the-art survey of anomaly detection techniques tailored for future networks. It focuses on two main technical approaches: Artificial Intelligence (AI)-based Intrusion Detection Systems (IDSs) and Runtime Verification (RV), a formal method for monitoring system behavior. In addition to exploring these methodologies, the report presents a real-world application: anomaly detection in smart home networks, with a focus on the Matter protocol.
Key findings of the report highlight the need for adaptive traffic representation, the combination of AI and formal methods, and the need to develop efficient detection technologies for real-world applications like smart homes. The report concludes with recommendations for future research directions to build scalable, efficient, and robust anomaly detection systems for next-generation networks.
