SIURU: A Framework for Machine Learning Based Anomaly Detection in IoT Network Traffic
Laura Lahesoo, Uyen Do, Rodrigo Matos Carnier, Kensuke Fukuda · 2023
With the increasing adoption of Internet of Things (IoT), research into anomaly detection (AD) in IoT network traffic is gaining importance. Malicious disturbances (e.g. malware and cyber attacks) and operational issues (e.g. software/sensor failures and physical damage) can seriously disrupt network operations. These problems can be detected with traffic monitoring and responded to with mitigation systems. In recent years, machine learning (ML) models have been successfully applied for AD, with state-of-the-art solutions reaching detection rates of . However, the heterogeneity of devices and anomalies in IoT networks poses a challenge for existing solutions. High AD accuracy is usually limited to the types of anomalies present in the training data, falling off significantly in the face of new anomalies. Many IoT-AD solutions are additionally limited to certain types of devices or require additional hardware setup.