A Feature Selection Method for Improved Clonal Algorithm Towards Intrusion Detection

作     者：Yin, Chunyong Ma, Luyu Feng, Lu 

作者机构：Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE》 (国际图形识别与人工智能杂志)

年 卷 期：2016年第30卷第5期

页      面：1659013-1659013页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD) Jiangsu Key Laboratory of Meteorological Observation and Information Processing [KDXS1105] Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET) 

主　　题：Artificial immune clonal algorithm intrusion detection feature selection 

摘      要：Intrusion detection is a kind of security mechanism which is used to detect attacks and intrusion behaviors. Due to the low accuracy and the high false positive rate of the existing clonal selection algorithms applied to intrusion detection, in this paper, we proposed a feature selection method for improved clonal algorithm. The improved method detects the intrusion behavior by selecting the best individual overall and clones them. Experimental results show that the feature selection algorithm is better than the traditional feature selection algorithm on the different classifiers, and it is shown that the final detection results are better than traditional clonal algorithm with 99.6% accuracy and 0.1% false positive rate.