Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly Likelihood
This work addresses the problem of real-time anomaly detection and prediction for system monitoring, but it appears incremental as it builds on existing anomaly detection methods with an added prediction component.
The authors tackled real-time anomaly detection and simultaneous prediction by developing a supervised machine learning model that processes data streams into time series and a Bayesian Network framework for prediction, achieving low latency.
We develop a supervised machine learning model that detects anomalies in systems in real time. Our model processes unbounded streams of data into time series which then form the basis of a low-latency anomaly detection model. Moreover, we extend our preliminary goal of just anomaly detection to simultaneous anomaly prediction. We approach this very challenging problem by developing a Bayesian Network framework that captures the information about the parameters of the lagged regressors calibrated in the first part of our approach and use this structure to learn local conditional probability distributions.