Isolation‐based Anomaly Detection

The first successful isolation‐based anomaly detector, i.e., iForest, uses trees as a means to perform isolation. Although it has been shown to have advantages over existing anomaly detectors, we have identified 4 weaknesses, i.e.,

  1. its inability to detect local anomalies;
  2. anomalies with a high percentage of irrelevant attributes;
  3. anomalies that are masked by axis‐parallel clusters; and
  4. anomalies in multimodal data sets.

To overcome these weaknesses, we created an alternative isolation mechanism is required and thus presents iNNE or isolation using Nearest Neighbour Ensemble. The latest source code of iForest and iNNE can be obtained from here.

Ye Zhu
Ye Zhu
Lecturer in IT

My research works focus on the fields of clustering and anomaly detection.