An Efficient Parallel Anomaly Detection Algorithm Based on Hierarchical Clustering

For the purpose of improving real time and profiles accuracy, a parallel anomaly detection algorithm based on hierarchical clustering has been proposed. Training and predicting are two busiest processes and they are parallel designed and implemented. Moreover, an abnormal cluster feature tree is built to dig anomalies from normal profiles. A series of experiment results on wellknown KDD Cup 1999 data sets indicate that the improved algorithm has superior performance in both detection and real time.