Explainable AI-Based Intrusion Detection Systems for IoT Environments: A Systematic Literature Review

Abstract

With the rapid proliferation of Internet of Things (IoT) technology today, the number of devices connected to each other through these networks is increasing significantly, and the security risks these devices face from cyberattacks have emerged as a clear problem. Intrusion detection systems (IDSs) are emerging as a solution to this problem and play a significant role in securing IoT-based networks. Although machine learning and deep learning-based IDS approaches have achieved high accuracy rates in detecting attacks in recent years, the lack of transparency in these models’ decision-making processes poses a major drawback in terms of reliability and explainability. To ensure that the decisions of IDS models are understandable and to address this issue, Explainable AI (XAI) approaches are being implemented. This study provides a detailed review of the current literature on XAI-enabled IDSs developed for IoT environments. The studies examined are systematically evaluated in terms of the deep learning models used, lightweight model designs, explainability methods and validations, approaches to protecting data privacy, and datasets. The literature review highlights that research is not only focused on the accuracy of attack detection but also aims to design IDS solutions that are lightweight, explainable, and privacy-preserving. This paper aims to provide a foundational resource to guide future research on reliable, explainable, and practical IoT intrusion detection systems by identifying the problems addressed in current research and highlighting the limitations in the literature. In addition, this work proposes and applies a study assessment framework to systematically assess methodological adequacy, experimental reproducibility, lightweight deployment, XAI techniques, and XAI validation.