Anomaly Detection Enhanced Classification in Computer Intrusion Detection
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
An approach to implement a network intrusion detection system using genetic algorithms
SAICSIT '04 Proceedings of the 2004 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Genetic Algorithm to Improve SVM Based Network Intrusion Detection System
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Intrusion detection using hierarchical neural networks
Pattern Recognition Letters
Optimization of Intrusion Detection through Fast Hybrid Feature Selection
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Layered approach for intrusion detection using naïve Bayes classifier
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Intrusion detection systems (IDSs) deal with large amounts of data containing irrelevant and/or redundant features. These features result in a slow training and testing process, heavy computational resources, and low detection accuracy. Features selection, therefore, is an important issue in IDSs. A reduced features set improves system accuracy and speeds up the training and testing process considerably. In this paper, we propose a novel and simple method-Enhanced Support Vector Decision Function (ESVDF)-for features selection. This method selects features based on two important factors: the feature's rank (weight), which is calculated using Support Vector Decision Function (SVDF), and the correlation between the features, which is determined by either the Forward Selection Ranking (FSR) or Backward Elimination Ranking (BER) algorithm. Our method significantly decreases training and testing times without loss in detection accuracy. Moreover, it selects the features set independently of the classifier used. We have examined the feasibility of our approach by conducting several experiments using the DARPA dataset. The experimental results indicate that the proposed algorithms can deliver satisfactory results in terms of classification accuracy, training time, and testing time.