Consolidating anomaly detection methods using hybrid artificial intelligence

A method comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• training, using historical anomaly prediction information, a hybrid artificial intelligence engine, wherein training the hybrid artificial intelligence engine configures the hybrid artificial intelligence engine to assign weight values to each of a plurality of anomaly detection engines and to select an anomaly detection result from the plurality of anomaly detection engines based on the weight values, wherein assigning the weight values comprises assigning, to each of the plurality of anomaly detection engines, a first weight value generated using statistical machine learning and a second weight value generated based on human intelligence;
• monitoring a system to collect system status information;
• inputting the system status information into each of the plurality of anomaly detection engines, wherein each of the plurality of anomaly detection engines outputs, based on the system status information, a corresponding binary value indicating whether the system status information is anomalous according to a given anomaly detection engine, and a corresponding confidence level associated with the corresponding binary value;
• inputting information of the plurality of anomaly detection engines into the hybrid artificial intelligence engine, wherein the hybrid artificial intelligence engine assigns the weight values to each of the plurality of anomaly detection engines and selects a binary value based on the weighting;
• comparing the selected binary value to a predetermined threshold value;
• based on identifying that the selected binary value is less than the predetermined threshold value, labeling the system as experiencing anomalous behavior;
• and
• executing one or more corrective actions to address the anomalous behavior.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• train, using historical anomaly prediction information, a hybrid artificial intelligence engine, wherein training the hybrid artificial intelligence engine configures the hybrid artificial intelligence engine to assign weight values to each of a plurality of anomaly detection engines and to select an anomaly detection result from the plurality of anomaly detection engines based on the weight values, wherein assigning the weight values comprises assigning, to each of the plurality of anomaly detection engines, a first weight value generated using statistical machine learning and a second weight value generated based on human intelligence;
• monitor a system to collect system status information;
• input the system status information into each of the plurality of anomaly detection engines, wherein each of the plurality of anomaly detection engines outputs, based on the system status information, a corresponding binary value indicating whether the system status information is anomalous according to a given anomaly detection engine, and a corresponding confidence level associated with the corresponding binary value;
• input information of the plurality of anomaly detection engines into the hybrid artificial intelligence engine, wherein the hybrid artificial intelligence engine assigns the weight values to each of the plurality of anomaly detection engines and selects a binary value based on the weighting;
• compare the selected binary value to a predetermined threshold value;
• based on identifying that the selected binary value is less than the predetermined threshold value, label the system as experiencing anomalous behavior;
• and
• execute one or more corrective actions to address the anomalous behavior.