Distributed Machine Learning Based Intrusion Detection in IoT - M, Meena; D, Sathish Kumar; M, Boovin; Doss S, Samuel Karunakara; K, Ganesh; A V, Sanjai (2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 2024)
IoT systems face significant security challenges due to their extensive attack surfaces and limited resources. To effectively counter these threats in real- time, intrusion detection algorithms must be both energy-efficient and compatible with the constrained computational capabilities of IoT devices (including memory, CPU, and storage). While deep learning approaches have proven effective for intrusion detection, their high computational demands make them impractical for resource-limited IoT devices. In this study, we propose developing a hierarchical model for IoT intrusion detection. This model will deploy machine learning (ML) algorithms tailored to the capabilities of IoT devices, while offloading more complex processing tasks to more powerful edge or cloud-based systems. These advanced systems will handle tasks such as updating ML model parameters and executing detection algorithms that exceed the capabilities of the IoT devices. Additionally, our approach will address not only traditional network attacks but also IoT-specific threats related to protocols such as REST, Zigbee, and MQTT.