A self-adaptive negative selection algorithm used for anomaly detection

查看全文 作　　者：Jinquan Zeng,Xiaojie Liu,Tao Li,Caiming Liu,Lingxi Peng,Feixian Sun Department of Computer Science,Sichuan University,Chengdu 610065,China

出　　处：《Progress in Natural Science:Materials International》索引2009年第19卷第2期,共6页

基　　金：supported by the National Natural Science Foundation of China (Grant No.60573130);the National High Technology Research and Development Program (Grant No.2006AA01Z435) 摘　　要：A novel negative selection algorithm (NSA),which is referred to as ANSA,is presented. In many actual anomaly detection systems,the training data are just partially composed of the normal elements,and the self/nonself space often varies over time. Therefore,anom-aly detection system has to build the profile of the system based on a part of self elements and adjust itself to adapt those variables. However,previous NSAs need a large number of self elements to build the profile of the system,and lack adaptability. In order to over-come these limitations,the proposed approach uses a novel technique to adjust the self radius and evolve the nonself-covering detectors to build an appropriate profile of the system. To determine the performance of the approach,the experiments with the well-known data-set were performed. Results exhibited that our proposed approach outperforms the previous techniques. 关键词：人工免疫系统不规则发现优选法则 NSA 医学免疫学

相关文献

参考文献（19）
引证文献（10）
耦合文献（31）

参考文献(19)

引证文献(10)

耦合文献(31)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

A self-adaptive negative selection algorithm used for anomaly detection

参考文献(19)

引证文献(10)

耦合文献(31)

用户提示