期刊
COMPUTERS & SECURITY
卷 24, 期 4, 页码 295-307出版社
ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.cose.2004.09.008
关键词
hybrid intelligent system; feature reduction; intrusion detection; ensemble design; Bayesian network; Markov blanket; decision trees
Current intrusion detection systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this study is to identify important input features in building an IDS that is computationally efficient and effective. We investigated the performance of two feature selection algorithms involving Bayesian networks (BN) and Classification and Regression Trees (CART) and an ensemble of BN and CART. Empirical results indicate that significant input feature selection is important to design an IDS that is lightweight, efficient and effective for real world detection systems. Finally, we propose an hybrid architecture for combining different feature selection algorithms for real world intrusion detection. (C) 2004 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据