Title :
A supervised intrusion detection method
Author :
Li, Qing-Hua ; Jiang, Sheng-Yi ; Li, Xin
Author_Institution :
Comput. Sch., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
A supervised intrusion detection method with new distance definition is proposed in this paper. This method based on constrained clustering, uses the produced clusters as classification model to predict which cluster the current data belongs to. The time complexity of the method is nearly linear with the size of dataset, the number of attributes and the final number of clusters. It is difference from existing supervised methods that our method can detect unknown intrusions. The experiment results on dataset KDDCUP99 demonstrate that the method has promising performance with high detection rate and low false alarm rate.
Keywords :
computational complexity; learning (artificial intelligence); pattern classification; pattern clustering; security of data; KDDCUP99 dataset; classification model; constrained clustering algorithm; low false alarm rate; supervised intrusion detection method; time complexity; Computer networks; Computer security; Data mining; Data security; Detection algorithms; Information security; Intrusion detection; Machine learning algorithms; Predictive models; Stability;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1382006
