E-RXAI-IoT: A Systematic Evaluation Framework of Rule-Based XAI Methods for Anomaly Detection in IoT Systems

The increasing complexity of Internet of Things (IoT) systems necessitates the development of interpretable and robust artificial intelligence (AI) techniques for anomaly detection. While prior work has explored deep learning-based explainable AI (XAI) methods, systematic studies evaluating rule-bas...

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Vydáno v:	IEEE access Ročník 13; s. 188730 - 188754
Hlavní autoři:	Namrita Gummadi, Anna, Abdelrahim, Elsaid Md, Gad, Ibrahim, Abdallah, Mustafa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy anchor Anomalies Anomaly detection Artificial intelligence Artificial neural networks coverage Datasets Decision trees Deep learning Explainable AI Explainable artificial intelligence fidelity Internet of Things IoT security Machine learning Measurement MEMS Microelectromechanical systems N-BaIoT precision Predictive models RuleFit Security Source code sparsity stability Support vector machines Systematics XAI evaluation metrics
ISSN:	2169-3536, 2169-3536
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Abstract	The increasing complexity of Internet of Things (IoT) systems necessitates the development of interpretable and robust artificial intelligence (AI) techniques for anomaly detection. While prior work has explored deep learning-based explainable AI (XAI) methods, systematic studies evaluating rule-based XAI approaches for IoT anomaly detection remain limited. In this paper, we fill this gap by investigating two state-of-the-art rule-based XAI methods-Anchor and RuleFit-across multiple machine learning models: Random Forest (RF), Decision Tree (DT), Deep Neural Network (DNN), and Support Vector Machine (SVM). We introduce a comprehensive evaluation framework that assesses these XAI methods using seven critical metrics mapped to AI and IoT security domains. These metrics are descriptive accuracy, sparsity, efficiency, stability, fidelity, coverage, and precision. We apply our framework to two distinct IoT datasets. The first one is the N-BaIoT dataset, which captures IoT botnet attacks (Mirai and Gafgyt) on commercial devices. The second is the MEMS dataset, which focuses on detecting anomalies in sensor readings from smart manufacturing processes. We perform a comprehensive evaluation of the seven XAI evaluation metrics for the two rule-based XAI methods for these two datasets. Through this study, we systematically highlight the strengths and limitations of rule-based XAI techniques in the context of IoT anomaly detection, providing practical insights into their interpretability and robustness. We have made the source code for our XAI evaluation framework publicly available for the community to foster reproducibility and encourage further research in this domain with more methods, metrics, and datasets.
AbstractList	The increasing complexity of Internet of Things (IoT) systems necessitates the development of interpretable and robust artificial intelligence (AI) techniques for anomaly detection. While prior work has explored deep learning-based explainable AI (XAI) methods, systematic studies evaluating rule-based XAI approaches for IoT anomaly detection remain limited. In this paper, we fill this gap by investigating two state-of-the-art rule-based XAI methods-Anchor and RuleFit-across multiple machine learning models: Random Forest (RF), Decision Tree (DT), Deep Neural Network (DNN), and Support Vector Machine (SVM). We introduce a comprehensive evaluation framework that assesses these XAI methods using seven critical metrics mapped to AI and IoT security domains. These metrics are descriptive accuracy, sparsity, efficiency, stability, fidelity, coverage, and precision. We apply our framework to two distinct IoT datasets. The first one is the N-BaIoT dataset, which captures IoT botnet attacks (Mirai and Gafgyt) on commercial devices. The second is the MEMS dataset, which focuses on detecting anomalies in sensor readings from smart manufacturing processes. We perform a comprehensive evaluation of the seven XAI evaluation metrics for the two rule-based XAI methods for these two datasets. Through this study, we systematically highlight the strengths and limitations of rule-based XAI techniques in the context of IoT anomaly detection, providing practical insights into their interpretability and robustness. We have made the source code for our XAI evaluation framework publicly available for the community to foster reproducibility and encourage further research in this domain with more methods, metrics, and datasets.
Author	Abdelrahim, Elsaid Md Namrita Gummadi, Anna Gad, Ibrahim Abdallah, Mustafa
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SubjectTerms	Accuracy anchor Anomalies Anomaly detection Artificial intelligence Artificial neural networks coverage Datasets Decision trees Deep learning Explainable AI Explainable artificial intelligence fidelity Internet of Things IoT security Machine learning Measurement MEMS Microelectromechanical systems N-BaIoT precision Predictive models RuleFit Security Source code sparsity stability Support vector machines Systematics XAI evaluation metrics
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Title	E-RXAI-IoT: A Systematic Evaluation Framework of Rule-Based XAI Methods for Anomaly Detection in IoT Systems
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