Isolation-Based Anomaly Detection

• Published in: ACM Transactions on Knowledge Discovery from Data
• Main Authors: Liu, Fei Tony, Ting, Kai Ming, Zhou, Zhi-Hua
• Other Authors: Ministry of Science and Technology of the People's Republic of China, National Natural Science Foundation of Jiangsu Province, National Natural Science Foundation of China
• Format: Article in Journal/Newspaper
• Language: English
• Published: Association for Computing Machinery (ACM) 2012
• Subjects: Orca
• Online Access: http://dx.doi.org/10.1145/2133360.2133363; https://dl.acm.org/doi/pdf/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 ( i Forest), 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, i Forest 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 i Forest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects. i Forest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample.