Isolation-based anomaly detection

• Published in: ACM Transactions on Knowledge Discovery from Data
• Main Authors: Liu, Fei, Ting, Kaiming, Zhou, Zhi-Hua
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2012
• Subjects: Information systems; Information systems applications; Data mining; Computing methodologies; Machine learning; Orca
• Online Access: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/34102; https://doi.org/10.1145/2133360.2133363

Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called isolation. This article proposes a method called Isolation Forest (iForest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally different from all existing methods. As a result, iForest is able to exploit subsampling (i) to achieve a low linear time-complexity and a small memory-requirement and (ii) to deal with the effects of swamping and masking effectively. Our empirical evaluation shows that iForest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects. iForest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample