Early Anomaly Detection in an Interconnected Power Grid and Communication Network: Exploiting Interdependent Structure of Failures

We study data fusion schemes for early detection of anomalies in an interconnected power grid and communication network, where power nodes rely on the real-time control via communication nodes, which in turn, depend on the former for power supply. Based on a key observation that failures are spatially correlated and propagate through neighboring nodes, we propose a data fusion scheme, which "scans" an anomalous cluster, i.e., a connected component of nodes, in each individual network. We show that the proposed scheme can detect weaker signals of anomalies, compared to baseline approaches, and further its detection capability increases with the size of the anomalous cluster. This finding leads us to further exploit the interdependent structure of failures across two networks and design a more powerful data fusion scheme, which jointly detects an anomalous cluster over the two interconnected networks. To analyze the detection gain of the joint data fusion scheme, we first characterize how quickly the anomalous behavior propagates, in both the power grid and the power- communication network, based on random graph and epidemic models. We then present numerical results to quantify the detection gain of the joint data fusion scheme.