Explainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification

The proliferation of Internet of Things (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 Machine Learning (ML) and Artificial Intelligence (AI). Most of the ML/AI-based intrusion detection techniques, though effective, often lack transparency and trustworthiness. To address these aspects, eXplainable Artificial Intelligence (XAI) has emerged as a promising solution, providing insights on AI model decisions. In this work, we describe an explainable Intrusion Detection System (IDS) in IoT networks which embeds a multi-way Fuzzy Decision Tree (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.