Explainable AI-based Intrusion Detection in the Internet of Things

The revolution of Artificial Intelligence (AI) has brought about a significant evolution in the landscape of cyberattacks. In particular, with the increasing power and capabilities of AI, cyberattackers can automate tasks, analyze vast amounts of data, and identify vulnerabilities with greater precision. On the other hand, despite the multiple benefits of the Internet of Things (IoT), it raises se-vere security issues. Therefore, it is evident that the presence of effcient intrusion detection mechanisms is critical. Although Ma-chine Learning (ML) and Deep Learning (DL)-based IDS have al-ready demonstrated their detection effciency, they still suffer from false alarms and explainability issues that do not allow security administrators to trust them completely compared to conventional signature/specification-based IDS. In light of the aforementioned remarks, in this paper, we introduce an AI-powered IDS with ex-plainability functions for the IoT. The proposed IDS relies on ML and DL methods, while the SHapley Additive exPlanations (SHAP) method is used to explain decision-making. The evaluation results demonstrate the effciency of the proposed IDS in terms of detection performance and explainable AI (XAI).