文章基本信息

标题：Improving the Performance of Multi-class Intrusion Detection Systems using Feature Reduction
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作者：Yasmen Mohamed Essam Eldin Wahba ; Ehab Elsalamouny ; Ghada Eltaweel 等
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2015
卷号：12
期号：3
出版社：IJCSI Press
摘要：Intrusion detection systems (IDS) are widely studied by researchers nowadays due to the dramatic growth in network-based technologies. Policy violations and unauthorized access is in turn increasing which makes intrusion detection systems of great importance. Existing approaches to improve intrusion detection systems focus on feature selection or reduction since some features are irrelevant or redundant which when removed improve the accuracy as well as the learning time. In this paper we propose a hybrid feature selection method using Correlation-based Feature Selection and Information Gain. In our work we apply adaptive boosting using nave Bayes as the weak (base) classifier. The key point in our research is that we are able to improve the detection accuracy with a reduced number of features while precisely determining the attack. Experimental results showed that our proposed method achieved high accuracy compared to methods using only 5-class problem. Correlation is done using Greedy search strategy and nave Bayes as the classifier on the reduced NSL-KDD dataset.
关键词：intrusion detection systems (IDS); feature selection; Correlation; Information Gain; Weka; AdaBoost