A self-adaptive negative selection algorithm used for anomaly detection  

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》2009年第2期261-266,共6页自然科学进展·国际材料（英文版）

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. 2008 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.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：Artificial immune system Anomaly detection Negative selection algorithm 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]