文献类型：会议论文

英文题名：A multi-mutation pattern immune network for intrusion detection

作者：Linhui, Zhao[1]; Xin, Fang[1]; Yaping, Dai[2]

第一作者：赵林惠

通讯作者：Linhui, Z.

机构：[1] School of Mechatronics, Beijing Union University, Beijing, 100020, China; [2] School of Computer and Control, Beijing Institute of Technology, Beijing , 100081, China

第一机构：北京联合大学机器人学院

通讯机构：[1]School of Mechatronics, Beijing Union University, Beijing, 100020, China|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

会议论文集：Proceedings of the 2008 4th International Conference on Information and Automation for Sustainability, ICIAFS 2008

会议日期：December 12, 2008 - December 14, 2008

会议地点：Colombo, Sri lanka

语种：英文

外文关键词：Intrusion detection - Sustainable development

摘要：Basing on the immune network theory and pattern recognition approach, A Multi-Mutation pattern immune network (MPIN) adaptive detector is proposed. By utilizing the immune response principle, the detection algorithm is designed. Because new features can be learnt by the MPIN in the real-time way, the detector is able to modify dynamically without periodical updating, and the detector's ability of identifying novel attacks are also improved. Combined with a template-adjustable decision templates fusion algorithm, a three-level-module adaptive intrusion detection system (TAIDS) is presented. Experiments are carried out on Fisher Iris dataset and KDDCUP- 99 database to verify the performance of this MPIN detector and TAIDS. Compared with the detection approach based on neural networks, the false positive rate is decreased by 17.43% and the detection accuracy of unknown attacks is increased by 24.27%. ? 2008 IEEE.