The isolation-based anomaly detector,isolation forest has two weaknesses,its inability to detect anomalies that were masked by axis-parallel clusters,and anomalies in high-dimensional data.An isolation mechanism based on random hyperplane and a multi-grained scanning was proposed to overcome these weaknesses.The random hyperplane generated by a linear combination of multiple dimensions was used to simplify the isolation boundary of the data model which was a random linear classifier that can detect more complex data patterns,so that the isolation mechanism was more consistent with data distribution characteristics.The multi-grained scanning was used to perform dimensional sub-sampling which trained multiple forests to generate a hierarchical ensemble anomaly detection model.Experiments show that the improved isolation forest has better robustness to different data patterns and improves the efficiency of anomaly points in high-dimensional data
