A Novel Data Mining Method for Network Anomaly Detection Based on Transductive Scheme

Authors:
Yang Li;Binxing Fang;Li Guo
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China and Graduate School of Chinese Academy of Sciences, Beijing 100080, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

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 we expect. Therefore, we 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.