Anomaly Detection in Network Management System Based on Isolation Forest

As an important part in network management system, rapid and accurate anomaly detection is one of the preconditions to guarantee the effective work of the network. In general, the network management system will monitor the data of multiple indicators, and the manifestations of network anomalies are complex in the data. Most existing anomaly detection approaches have encountered some trouble in network anomaly detection because of the periodicity of time series data or high time complexity and memory cost working on high-dimensional data. Aiming at the deficiency of present methods of network anomaly detection, we propose an adapted method based on iForest algorithm. We construct some feature extractors through statistical methods to highlight different abnormal behaviors in different indicator and then use the extracted feature data for the construction and prediction of iForest. Combining specific feature extractors, we can eliminate the effects of periodicity, or specify the detection of peaks or troughs to adapt to different indicators. By means of iForest algorithm which has linear time complexity and low memory requirement, our method makes a rapid detection with large dataset and works well in high dimensional network management data.