Bayesian Anomaly Detection Using Extreme Value Theory

Autor:	Guggilam, Sreelekha, Zaidi, S. M. Arshad, Chandola, Varun, Patra, Abani
Rok vydání:	2019
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicability of such methods, since most methods are sensitive to the choice of the threshold, and it is challenging to set optimal thresholds. We present a probabilistic framework to explicitly model the normal and anomalous behaviors and probabilistically reason about the data. An extreme value theory based formulation is proposed to model the anomalous behavior as the extremes of the normal behavior. As a specific instantiation, a joint non-parametric clustering and anomaly detection algorithm is proposed that models the normal behavior as a Dirichlet Process Mixture Model. Comment: 7 pages, 7 figures, The paper has been withdrawn due to major modification in the automation model
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1905.12150 Zobrazit plný text záznamu View this record from Arxiv