Anomaly Detection with Artificial Immune Network  

作　　者：PENG Lingxi LI Tao LIU Xiaojie CHEN Yuefeng LIU Caiming LIU Sunjun 

机构地区：[1]College of Computer Science, Sichuan University, Chengdu 610065, Sichuan, China [2]School of Information, Guangdong Ocean University, Zhanjiang 524025, Guangdong, China

出　　处：《Wuhan University Journal of Natural Sciences》2007年第5期951-954,共4页武汉大学学报（自然科学英文版）

基　　金：Supported by the National High Technology Research and Development Program of Chin(a863 Program)(2006AA01Z435);the National Natural Science Foundation of China (60573130, 60502011).

摘　　要：Inspired by the immune network theory, an adaptive anomaly detection paradigm based on artificial immune network, referred as APAI, is proposed. The implementation of the paradigm includes： initially, the first is to create the initial antibody network; then, through the learning of each training antigen, the antibody network is evolved and updated by the optimal antibodies. Finally, anomaly detection process is accomplished by majority vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study, which is taken from the UCI machine learning database. The obtained detection accuracy of APAI was 97.7%, which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classification properties, APAI possesses biological immune network properties such as clonal selection, immune network, and immune memory, which can be applied to pattern recognition, classification, and etc.Inspired by the immune network theory, an adaptive anomaly detection paradigm based on artificial immune network, referred as APAI, is proposed. The implementation of the paradigm includes： initially, the first is to create the initial antibody network; then, through the learning of each training antigen, the antibody network is evolved and updated by the optimal antibodies. Finally, anomaly detection process is accomplished by majority vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study, which is taken from the UCI machine learning database. The obtained detection accuracy of APAI was 97.7%, which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classification properties, APAI possesses biological immune network properties such as clonal selection, immune network, and immune memory, which can be applied to pattern recognition, classification, and etc.

关 键 词：anomaly detection artificial immune network machine learning CLASSIFICATION 

分 类 号：TP305[自动化与计算机技术—计算机系统结构]