A self region based real-valued negative selection algorithm

作     者：张凤斌 王大伟 王胜文 

作者机构：Dept.of Computer Science and TechnologyHarbin University of Science & Technology Dept.of Computer Science and TechnologyTsinghua University 

出 版 物：《Journal of Harbin Institute of Technology(New Series)》 (哈尔滨工业大学学报（英文版）)

年 卷 期：2008年第15卷第6期

页      面：851-855页

核心收录：

学科分类：0839[工学-网络空间安全] 08[工学] 

基　　金：Sponsored by the National Natural Science Foundation of China (Grant No. 60671049) the Subject Chief Foundation of Harbin (Grant No.2003AFXXJ013) the Education Department Research Foundation of Heilongjiang Province(Grant No. 10541044, 1151G012) the Postdoctor Foundation of Heilongjiang Province(Grant No.LBH-Z05092) 

主　　题：artificial immune real-valued negative selection cluster analysis self region partial training 

摘      要：Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection *** solve this problem,a self region based real-valued negative selection algorithm is *** this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are *** algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data *** show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.