Machine learning based anomaly detection

摘要: The technology disclosed relates to machine learning based anomaly detection. In particular, it constructing activity models on per-tenant and per-user basis using an online streaming learner that transforms unsupervised problem into a supervised by fixing target label regressor without constant or intercept. Further, detecting anomalies in near real-time streams of security-related events one more tenants transforming the categorized features requiring loss function analyzer correlate, essentially through origin, with feature artificially labeled as constant. It further includes determining score for production event calculated likelihood coefficients feature-value pairs prevalencist probability value comprising coded features-value pairs.