MGG-AD: Multi-Granularity Graph-Based Anomaly Detection in IoT Systems

Abstract

Internet of Things (IoT) systems gained significant attention for monitoring and optimizing processes. To ensure realtime detections with low latency, IoT applications often monitor individual components through a microservice network, deployed close to IoT devices. Existing methods for multivariate time series anomaly detection typically construct one global graph for identifying deviations in predicted or reconstructed attribute features. However, consider an active node that suddenly experiences a sharp drop in connections or established unexpected links; these structural anomalies would be totally overlooked. To address these limitations, this paper proposes Multi-Granularity Graph Anomaly Detection (MGG-AD), a novel approach that captures both attribute and topological dependencies within IoT systems. Specifically, we construct a multi-granularity dependency graph from a global graph and multiple local subgraphs that define geographical correlations among IoT devices. First, at the attribute-level, we detect contextual deviations by reconstructing feature representations and contrasting attributes across local subgraphs. Second, at the topological-level, we identify abnormal structural variations by comparing local subgraphs with the global graph-called contrastive learning. We evaluated MGGAD on two publicly available datasets and against state-of-the-art methods-we found that our solution provides higher detection accuracy and robustness, underlining its suitability for dynamic IoT systems.