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arXiv Machine Learning By Dalila Khettaf, Djamel Djenouri, Zeinab Rezaeifar, Youcef Djenouri

Clustering-Based Collective Anomaly Detection in IoT Systems: A Graph Neural Network Approach

Read the original on arXiv Machine Learning →

The paper introduces Unsupervised Graph Collective Anomaly Detection (UGCAD), a framework that uses a variational graph autoencoder to learn graph representations of IoT network traffic and then enhances clustering to group nodes. UGCAD identifies collective anomalies by aggregating normal clusters and applying anomaly scores to the refined groups. Experiments on CICIoT2023 and ToN-IoT datasets show that UGCAD outperforms traditional and state‑of‑the‑art clustering‑based CAD methods in both clustering quality and anomaly detection accuracy.

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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