Entropy isolation forest based on dimension entropy for anomaly detection

Abstract

Anomaly detection, as an important basic research task in the field of data mining, has been concerned by both industry and academia. Among many anomaly detection methods, iForest (isolation Forest) has low time complexity and good detection effect. It has better adaptability in the face of high-capacity and high-dimensional data. However, iForest is not suitable for the special high-dimensional data, is not stable enough, and is not so robust to the noise features. In view of these problems, this paper proposes an improved anomaly detection method E-iForest (entropy-isolation forest) based on dimension entropy. By introducing the dimension entropy as the basis for selecting the isolation attribute and the isolation point during the training process, the method uses three isolation strategies and adjust the path length calculation. The experiments show that the E-iForest has better detection effect, has better speed in high-capacity datasets, is more stable than iForest and is more robust to the noise features.