Anomaly Detection and Localisation using Mixed Graphical Models

TL;DR

This paper introduces a novel method for anomaly detection and localization in heterogeneous data using mixed graphical models, leveraging a conditional likelihood ratio and CUSUM algorithm for precise, variable-level anomaly identification.

Contribution

The paper presents a new approach combining mixed graphical models with a CUSUM-based detection method for effective anomaly localization in heterogeneous data streams.

Findings

Effective variable-level anomaly detection demonstrated

Improved localization accuracy over univariate methods

Method applicable to data streams with mixed data types

Abstract

We propose a method that performs anomaly detection and localisation within heterogeneous data using a pairwise undirected mixed graphical model. The data are a mixture of categorical and quantitative variables, and the model is learned over a dataset that is supposed not to contain any anomaly. We then use the model over temporal data, potentially a data stream, using a version of the two-sided CUSUM algorithm. The proposed decision statistic is based on a conditional likelihood ratio computed for each variable given the others. Our results show that this function allows to detect anomalies variable by variable, and thus to localise the variables involved in the anomalies more precisely than univariate methods based on simple marginals.