Anomaly detection in large-scale networks: A state-space decision process

Abstract

A new data fusion and network analytics framework is proposed that is based on the topology of large-scale networks and the stochastic dependencies between nodes, edges, and sensor data. The framework can transform real-time sensor data collected from disparate sources in a network to detect the location of anomalies and the nodes that are impacted by the detected anomalies. By intelligently fuzing multidimensional sensor data based on the topology of a large-scale network, this article also contributes to big data analytics for network systems. We will show that the proposed framework not only brings computational benefits, but also results in better anomaly estimates leading to lower false alarm rates and higher detection rates.