Clustering-Based Collective Anomaly Detection in IoT Systems: A Graph Neural Network Approach
The rapid advancement of Internet of Things (IoT) technology has led to the widespread deployment of smart, interconnected devices across a range of domains. However, this expansion has also resulted in a substantial increase in network traffic, creating more opportunities for malicious actors to launch cyberattacks and compromise sensitive information, thereby increasing the need for effective anomaly detection. The state-of-the-art in anomaly detection has predominantly focused on point anomalies. In contrast, the detection of collective anomalies remains relatively under-explored in the literature. In this paper, we introduce Unsupervised Graph Collective Anomaly Detection (UGCAD), a n...
| Authors | Dalila Khettaf, Djamel Djenouri, Zeinab Rezaeifar et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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