An Efficient Parallel Anomaly Detection Algorithm Based on Hierarchical Clustering
Ren Wei-wu, Liang Hu, Zhao Kuo, Chu Jianfeng · Journal of Networks · 2013
Abstract—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 well-known KDD Cup 1999 data sets indicate that the improved algorithm has superior performance in both detection and real time. Index Terms—parallel algorithm; hierarchy clustering; abnormal cluster feature tree; normal profiles I.