Method for determining optimal anomaly detection model for processing input data

A method for detecting anomaly of data using network function performed by one or more processors, comprising:

• generating, by a processor, an anomaly detection model including a plurality of anomaly detection sub models having a pre-learned network function through using a plurality of training data subsets included in a training data set;
• determining, by the processor, one or more anomaly detection sub models for calculating an input data among the plurality of anomaly detection sub models,
• wherein the operation of determining one or more anomaly detection sub models for calculating an input data among the generated anomaly detection sub models includes an operation of determining one or more anomaly detection sub models for calculating the input data based on at least one of processing tendency of other input data for the plurality of anomaly detection sub models, context information of the input data, or cluster information of the input data;
• judging, by the processor, whether or not anomaly exists in the input data through using the one or more anomaly detection sub model,
• clustering a plurality of anomaly detection sub models, and
• generating an integrated anomaly detection sub model through integrating one or more anomaly detection sub models included in one cluster.

A computing device, including:

• one or more processors;
• and
• a memory storing commands executable in processor,
• wherein the processor is configured to:
• generate an anomaly detection model including a plurality of anomaly detection sub models having a pre-learned network function through using a plurality of training data subsets included in a training data set;
• determine one or more anomaly detection sub models for calculating an input data among the plurality of anomaly detection sub models,
• wherein the determining one or more anomaly detection sub models for calculating an input data among the generated anomaly detection sub models includes determining one or more anomaly detection sub models for calculating the input data based on at least one of processing tendency of other input data for the plurality of anomaly detection sub models, context information of the input data, or cluster information of the input data;
• judge whether or not anomaly exists in the input data through using the one or more anomaly detection sub model,
• clustering a plurality of anomaly detection sub models, and
• generating an integrated anomaly detection sub model through integrating one or more anomaly detection sub models included in one cluster.