A Unified XAI-NSAI Analytical Framework for Trustworthy IoT Anomaly Detection

arXiv:2610.41424v1 Announce Type: new Abstract: The widespread deployment of Internet of Things (IoT) devices in critical environments makes anomaly detection essential for identifying abnormal behaviors and supporting timely investigation and response. However, accurate detection alone is insufficient. Practical IoT deployments must also provide explanations, incorporate domain knowledge and reasoning, operate under IoT constraints, and support human decision making. Although recently proposed approaches increasingly incorporate Explainable Artificial Intelligence (XAI) or Neuro-Symbolic Artificial Intelligence (NSAI), XAI and NSAI methods are often considered independently. We argue that they should be considered together to better understand their complementary roles in supporting trustworthy IoT anomaly detection. To address this gap, we propose a unified analytical framework organized around five complementary dimensions. The framework provides a structured basis for characterizing existing approaches, identifying capability gaps and design trade-offs, and guiding the design of new anomaly detection systems for different IoT applications. We demonstrate the framework's analytical utility by applying it to three representative studies and illustrate its design utility through its application to a Medical IoT anomaly detection system.

Abstract:The widespread deployment of Internet of Things (IoT) devices in critical environments makes anomaly detection essential for identifying abnormal behaviors and supporting timely investigation and response. However, accurate detection alone is insufficient. Practical IoT deployments must also provide explanations, incorporate domain knowledge and reasoning, operate under IoT constraints, and support human decision making. Although recently proposed approaches increasingly incorporate Explainable Artificial Intelligence (XAI) or Neuro-Symbolic Artificial Intelligence (NSAI), XAI and NSAI methods are often considered independently. We argue that they should be considered together to better understand their complementary roles in supporting trustworthy IoT anomaly detection. To address this gap, we propose a unified analytical framework organized around five complementary dimensions. The framework provides a structured basis for characterizing existing approaches, identifying capability gaps and design trade-offs, and guiding the design of new anomaly detection systems for different IoT applications. We demonstrate the framework's analytical utility by applying it to three representative studies and illustrate its design utility through its application to a Medical IoT anomaly detection system.

Machine Learning (cs.LG)

arXiv:2610.41424 [cs.LG]

(or arXiv:2610.41424v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.41424 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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A Unified XAI-NSAI Analytical Framework for Trustworthy IoT Anomaly Detection

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