Compressive anomaly detection in large networks

This paper considers a large sensor network with its nodes taking measurements from certain distributions, while a small subset of the nodes draw anomalous measurements from distributions that differ from the majority. Since all the distributions are unknown a priori, the compressive anomaly detection (CAD) algorithm is proposed at the fusion center to identify the set of anomalous sensors and estimate both the common and anomaly distributions, using only few compressed sensor observations under the type-based multiple access (TB-MA) protocol. Simulations demonstrate that the proposed CAD algorithm can efficiently single out the set of anomalies and estimate the distributions accurately.