An introduction to variable and feature selection
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Handling Nominal Features in Anomaly Intrusion Detection Problems
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Interactive visualization for network and port scan detection
RAID'05 Proceedings of the 8th international conference on Recent Advances in Intrusion Detection
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In many applications, one has to actively select among a set of expensive observations before making an informed decision. In this paper, we describe a hybrid of a simple artificial intelligence algorithm and a method based on class separability applied to the selection of feature subsets for classication problems. The method allows an expert to discover informative features for separation of normal and attack instances. Experiments performed on the KDD Cup dataset show that explanations provided by the method reveal the nature of attacks. Application of the method for feature selection yields a major improvement of detection accuracy.