Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble

• Published in: 2014 IEEE International Conference on Data Mining Workshop pp. 698 - 705
• Main Authors: Bandaragoda, Tharindu R., Kai Ming Ting, Albrecht, David, Liu, Fei Tony, Wells, Jonathan R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2014
• Subjects: Accuracy; anomaly detection; Bismuth; Detectors; Educational institutions; ensemble-based; Estimation; nearest-neighbour; Time complexity; Training
• ISSN: 2375-9232; 2375-9259
• DOI: 10.1109/ICDMW.2014.70

This paper presents iNNE (isolation using Nearest Neighbour Ensemble), an efficient nearest neighbour-based anomaly detection method by isolation. Inne runs significantly faster than existing nearest neighbour-based methods such as Local Outlier Factor, especially in data sets having thousands of dimensions or millions of instances. This is because the proposed method has linear time complexity and constant space complexity. Compared with the existing tree-based isolation method iForest, the proposed isolation method overcomes three weaknesses of iForest that we have identified, i.e., Its inability to detect local anomalies, anomalies with a low number of relevant attributes, and anomalies that are surrounded by normal instances.