Relevance feature selection via analysis of the KDD '99 intrusion detection dataset

Abstract

The rapid development of business and othertransaction systems over the Internet makes computer securitya critical issue. In recent times, data mining and machine learning have been subjected to extensive research in intrusion detection with emphasis on improving the accuracy of detection classifier. But selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. In this paper, we presented therelevance of each feature in KDD ’99 intrusion detection dataset to the detection of each class. Rough set degree of dependency and dependency ratio of each class were employed to determine the most discriminating features for each class. Empirical results show that seven features were not relevant in the detection of any class.