Metamodel and feature generation for rapid and accurate anomaly detection

A method comprising performing, by a computer system:

• receiving a data set comprising a plurality of data values, wherein the plurality of data values are labeled;
• performing a feature extraction process on the plurality of data values, thereby producing a plurality of data vectors, each data vector comprising a plurality of feature values;
• for each data vector of the plurality of data vectors:
• determining a width dimension, a height dimension, and a depth dimension based on a number of feature values in the plurality of feature values in the data vector;
• generating one or more value maps comprising a plurality of value cells, wherein a number of value maps in the one or more value maps is equal to the depth dimension, wherein a width of each value map of the one or more values maps is equal to the width dimension, and wherein a height of each value map of the one or more value maps is equal to the height dimension;
• populating the one or more value maps using the plurality of feature values by assigning the plurality of feature values to the plurality of value cells;
• generating, based on the one or more value maps, one or more color maps comprising a plurality of color cells, each color cell of the plurality of color cells associated with a color value corresponding to a feature value, wherein each color map is associated with a particular color channel of one or more color channels;
• generating a unified color map, the unified color map comprising the one or more color maps, thereby generating a plurality of unified color maps;
• and
• training a machine learning model using the plurality of unified color maps and a plurality of labels corresponding to the plurality of data values.

A computer system comprising:

• a processor;
• and
• a non-transitory computer readable medium coupled to the processor, the non-transitory computer readable medium comprising code, executable by the processors for implementing a method for training a target model to classify a plurality of target data values as normal or anomalous, the method comprising:
• generating using one or more source data sets, a plurality of source sub-sets, wherein the one or more source data sets comprise a plurality of source data values, wherein each source sub-set comprises a sub-set of source data values from the plurality of source data values, and wherein the source data values are labeled;
• training a plurality of sub-models corresponding to the plurality of source sub-sets to classify the plurality of source data values in the plurality of source sub-sets, thereby producing a plurality of loss functions, the plurality of loss functions relating a plurality of performance metrics to a plurality of sub-model parameter sets, wherein each performance metric of the plurality of performance metrics and each sub-model parameter set of the plurality of sub-model parameter sets corresponds to a sub-model of the plurality of sub-models;
• determining, based on the plurality of loss functions, an estimate parameter set;
• and
• training the target model using a target data set and the estimate parameter set, thereby generating a target parameter set corresponding to the target model, wherein training the target model enables the target model to be used to classify the plurality of target data values as normal or anomalous.