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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access Jg. 13; S. 188730 - 188754
Hauptverfasser:	Namrita Gummadi, Anna, Abdelrahim, Elsaid Md, Gad, Ibrahim, Abdallah, Mustafa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!