Systems and methods for semi-supervised anomaly detection through ensemble stacking

A computer-implemented method, comprising:

• obtaining a training dataset including data representative of a plurality of interactions;
• generating a plurality of anomaly detection models, wherein at least one of the plurality of anomaly detection models is generated by an unsupervised training process;
• generating a unified anomaly score by combining outputs of a subset of the plurality of anomaly detection models;
• generating an augmented training dataset by labeling at least one of the interactions in the plurality of interactions based on the unified anomaly score;
• and
• generating an anomaly classification model by applying a supervised training process including the augmented training dataset.

A non-transitory computer readable medium having instructions stored thereon that, when executed by one or more processors, cause one or more devices to perform operations comprising:

• generating a plurality of anomaly detection models, wherein at least one of the plurality of anomaly detection models is generated by an unsupervised training process;
• generating a unified anomaly score by combining outputs of a subset of the plurality of anomaly detection models;
• generating an augmented training dataset by labeling at least one of the interactions in the plurality of interactions based on the unified anomaly score;
• and
• generating an anomaly classification model by applying a supervised training process including the augmented training dataset.