Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Intrusion detection with neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Naive Bayes vs decision trees in intrusion detection systems
Proceedings of the 2004 ACM symposium on Applied computing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A detailed analysis of the KDD CUP 99 data set
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
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In this paper, we address the dataset shift problem in building intrusion detection systems by assuming that network traffic variants follow the covariate shift model. Based on two recent works on direct density ratio estimation which are kernel mean matching and unconstrained least squares importance fitting, we propose to modify two well-known classification techniques: neural networks with back propagation and support vector machine in order to make these techniques work better under covariate shift effect. We evaluated the modified techniques on a benchmark intrusion detection dataset, the KDD Cup 1999, and got higher results on predication accuracy of network behaviors compared with the original techniques.