An Anomaly Detection Algorithm for Multi-dimensional Segmentation Plane Isolation Forest

Abstract

Traditional anomaly detection is mostly based on single-dimensional data for identification and analysis. The isolated forest algorithm is not a description of normal samples, but a field is divided by isolated anomalies. This paper introduces a multi-dimensional segmentation plane anomaly detection algorithm for isolated forest. By using multi-dimensional plane segmentation in the process of constructing hyperplane segmentation, and controlling the generation process of the isolated tree, the generation process of the isolated forest is optimized. By adding some common datasets for comparative experiments, good results have been achieved