文章基本信息

标题：HYBRID FEATURE SELECTION ALGORITHM FOR INTRUSION DETECTION SYSTEM
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作者：Hasani, Seyed Reza ; Othman, Zulaiha Ali ; Kahaki, Seyed Mostafa Mousavi 等
期刊名称：Journal of Computer Science
印刷版ISSN：1549-3636
出版年度：2014
卷号：10
期号：6
页码：1015-1025
DOI：10.3844/jcssp.2014.1015.1025
出版社：Science Publications
摘要：Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS) are increasing incredibly in Information Security research using Soft computing techniques. In the previous researches having irrelevant and redundant features are recognized causes of increasing the processing speed of evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates dimension of data and reduce redundancy and ambiguity caused by none important attributes. Therefore, feature selection methods are well-known methods to overcome this problem. There are various approaches being utilized in intrusion detections, they are able to perform their method and relatively they are achieved with some improvements. This work is based on the enhancement of the highest Detection Rate (DR) algorithm which is Linear Genetic Programming (LGP) reducing the False Alarm Rate (FAR) incorporates with Bees Algorithm. Finally, Support Vector Machine (SVM) is one of the best candidate solutions to settle IDSs problems. In this study four sample dataset containing 4000 random records are excluded randomly from this dataset for training and testing purposes. Experimental results show that the LGP_BA method improves the accuracy and efficiency compared with the previous related research and the feature subcategory offered by LGP_BA gives a superior representation of data.
关键词：IDS; Linear Genetic Programming; Feature Selection; Bees Algorithm; Anomaly Detection