Consolidating anomaly detection methods using hybrid artificial intelligence

Abstract

A computing platform may monitor a system to collect system status information. The computing platform may input the system status information into each of a plurality of anomaly detection engines, which may each output, based on the system status information, a corresponding binary value indicating whether the system status information is anomalous according to the given anomaly detection engine, and a corresponding confidence level. The computing platform may input information of the plurality of anomaly detection engines into a hybrid artificial intelligence engine, which may assign the weight values to each of the plurality of anomaly detection engines and selects a binary value based on the weighting. Based on identifying that the selected binary value is less than a predetermined threshold value, the computing platform may label the system as experiencing anomalous behavior. The computing platform may execute one or more corrective actions to address the anomalous behavior.