Anomaly Detection via Semantically Conjugate View Learning on Industrial Temporal Data

Anomaly detection based on knowledge discovery from the industrial temporal data via graph neural networks (GNNs) has been extensively prevailing over the past decade. So far, massive contributions have been made in deriving superior anomaly detection solutions by leveraging the sophisticated associativity among nodes in a graph topology. While this node-oriented fashion is at its fancy, the idea of utilizing a graph’s edges in anomaly detection tends to be equivalently significant, in that the edges are the other core component that construct a graph and are, more essentially, rich in convoluted correlational properties. As current methods seldom take these edge-level correlations into account, we aim at constructing a dual-channel graph learning scheme attempting to adequately utilize these edge-level contextual semantics in anomalous pattern detection. In specific, we design and develop the node-edge conjugate network (NECN), a GNN-based solution that conducts devicewise anomaly detection leveraging not only the complex associativity among the nodes but also the sophisticated correlations among the edges via knowledge discovery from the industrial temporal data. With the in-depth contextual features of the nodes and edges profiled in their respective channel, the resulting embeddings are a more appropriate reflection of the graph’s topological properties in terms of both nodes and edges, and hence serve as a more solid basis for subsequent anomaly detection. The NECN’s effectiveness in achieving a superior anomaly detection accuracy is demonstrated in a comprehensive comparative analysis with multiple state-of-the-art baselines over three popular datasets specifically developed for the study of ICS security.