Turkish Journal of Electrical Engineering and Computer Sciences




In recent years, the rapid growth of the Internet of Things (IoT) has raised concerns about the security and reliability of IoT systems. Anomaly detection is vital for recognizing potential risks and ensuring the optimal functionality of IoT networks. However, traditional anomaly detection methods often lack transparency and interpretability, hindering the understanding of their decisions. As a solution, Explainable Artificial Intelligence (XAI) techniques have emerged to provide human-understandable explanations for the decisions made by anomaly detection models. In this study, we present a comprehensive survey of XAI-based anomaly detection methods for IoT. We review and analyze various XAI techniques, including feature-based approaches, model-agnostic methods, and post-hoc explainability techniques, and discuss their applicability and limitations in the context of IoT. We also discuss the challenges and future research directions in XAI-based anomaly detection for IoT. This survey aims to provide researchers and practitioners in the field of IoT security with a better understanding of the current state of XAI techniques and their potential for enhancing anomaly detection in IoT systems.


Internet of Things (IoT), anomaly detection, explainable artificial intelligence (XAI), interpretable machine learning (IML)

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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.