Enhancing anomaly detection with adaptive node inspection in large-scale networks with binary sensors

Abstract

Maintaining the stability and reliability of large-scale networks and graph structures is a practical challenge, particularly in sensor-intensive systems. One critical task in such networks is to identify anomalies and the origin of disturbances in a timely manner. However, successful anomaly detection requires sufficient and accurate data from sensors across the network. This work aims to develop an innovative framework to improve the accuracy of anomaly detection tasks with binary sensor data through intelligent node selection and inspection. Instead of relying only on stochastic insights obtained from network sensors, we explore how a specific small set of nodes can be inspected using a Bayesian framework so that the anomaly detection performance is improved. We demonstrate the effectiveness of the proposed model with a set of numerical experiments.