Date Added: Nov 2010
Network anomaly detection has been a hot topic in the past years. However, high false alarm rate, difficulties in obtaining exact clean data for the modeling of normal patterns and the deterioration of detection rate because of "Unclean" training set always make it not as good as the authors expect. Therefore, they propose a novel data mining method for network anomaly detection in this paper. Experimental results on the well-known KDD Cup 1999 dataset demonstrate it can effectively detect anomalies with high true positives, low false positives as well as with high confidence than the state-of-the-art anomaly detection methods. Furthermore, even provided with not purely "Clean" data (unclean data), the proposed method is still robust and effective.