Feature Subset Selection Using Genetic Algorithm for Intrusion Detection System

被引：1
作者：
Behjat, Amir Rajabi [1 ]
Vatankhah, Najmeh [1 ]
Mustapha, Aida [1 ]
机构：
[1] Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Upm Serdang 43400, Selangor, Malaysia
来源：
ADVANCED SCIENCE LETTERS | 2014年 / 20卷 / 01期
关键词：
Intrusion Detection System; KDD; 99; Dataset; Genetic Algorithm; Feature Selection;
D O I：
10.1166/asl.2014.5270
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
KDD 99 intrusion detection datasets, which are based on DARPA 98, is a labeled dataset studied in the field of intrusion detection. The respected KDD dataset contains numerous features, in which some of them are irrelevant or less effective to detect the attacks. This study is set to improve the classification of intrusions by means of selecting significant features. Binary Genetic Algorithm (BGA) is proposed for feature selection in order to decrease the number of unrelated features. The selected features are then become the input for the classification task using a standard Multi-layer Perceptron (MLP) classifier. The results achieved show very high classification accuracy and low false positive rate with the lowest CPU time.
引用
收藏
页码：235 / 238
页数：4
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