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arXiv Machine Learning By Hugues Leger, Giovani R. Abuaitah

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

Read the original on arXiv Machine Learning →

The paper introduces a unified analytical framework that combines Explainable Artificial Intelligence (XAI) and Neuro‑Symbolic Artificial Intelligence (NSAI) for trustworthy anomaly detection in Internet of Things (IoT) systems. It outlines five complementary dimensions to evaluate existing methods, identify gaps, and guide the design of new detection systems. The authors demonstrate the framework’s usefulness by applying it to three studies and a medical IoT anomaly detection case.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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