A Scalable Bayesian Framework for Large-Scale Sensor-Driven Network Anomaly Detection

Abstract

Many real systems have a network/graph structure with many connected nodes and many edges representing deterministic or stochastic dependencies and interactions between nodes. Various types of known or unknown anomalies and disturbances may occur across these networks over time. Developing real-time anomaly detection and isolation frameworks is crucial to enable network operators to make more informed and timely decisions and take appropriate maintenance and operations actions. To monitor the health of modern networks in real time, different types of sensors and smart devices are installed across these networks that can track real-time data from a particular node or a section of a network. In this paper, we introduce an innovative inference method to calculate the most probable explanation of a set of hidden nodes in heterogeneous attributed networks with a directed acyclic graph structure represented by a Bayesian network, given the values of a set of binary data observed from available sensors, which may be located only at a subset of nodes. The innovative use of Bayesian networks to incorporate parallelization and vectorization makes the proposed framework applicable for large-scale graph structures. The efficiency of the model is shown through a comprehensive set of numerical experiments.