Explainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification
Pietro Ducange, Michela Fazzolari, Francesco Marcelloni · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 2024
The proliferation of IoT devices in our daily lives has raised concerns about the security of transmitted data. Due to their limited resources, IoT devices are vulnerable to malware attacks and cyber threats. Detecting and classifying these attacks is crucial to mitigate their impact. Various intrusion detection techniques have been proposed for IoT, including approaches based on ML and AI. Most of the ML/AI-based intrusion detection techniques, though effective, often lack transparency and trustworthiness. To address these aspects, XAI has emerged as a promising solution, providing insights on AI model decisions. In this work, we describe an explainable IDS in IoT networks which embeds a multi-way FDT as an XAI model for traffic classification. We propose a Cross-Device training and evaluation approach in which we evaluate the generalization capability of the IDS when new devices are connected to the IoT network without retraining the FDT.