Isolation-Based Anomaly Detection

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,…

• NN
  National Natural Science Foundation of China

  Award: 6.11E+15
• MO
  Ministry of Science and Technology of the People's Republic of China

  Award: 2010CB327903
• NS
  Natural Science Foundation of Jiangsu Province

  Award: BK2008018