Abstract: A computer-implemented method for anomaly detection based on deep learning includes acquiring a plurality of records, each record having a corresponding number of attributes, identifying outliers in the plurality of records using labels generated from processing the plurality of records through an ensemble of different deep learning models, wherein an output of at least one model is used as an input to at least one other model and detecting anomalies in the plurality of records using a probabilistic classifier based on plurality of records and labels.

Abstract: A technique includes acquiring a plurality of records, each record having a corresponding number of attributes determining, based on local density measurements for numeric and normally distributed attribute value frequency measure for categorical attributes tags in the training portion of the plurality of records which is then used in probabilistic classifier for anomaly detection. A second set of implementations is proposed using ensemble method of combining deep learning algorithms for the same.