ERRORS ESTIMATING OF INCOMPLETION AND UPDATING STRATEGY IN IDS

摘要：

Because there existed the problem of incomplete training sets in current intrusion detection systems, it results in false positive errors. In the paper, an ID model-IAIDM (Immune-based Adaptive Intrusion Detection Model) is firstly put forward. Based on the characteristics of IAIDM, a analytical method of discrete random process is introduced to estimate the ratio of false positive errors. The analytical results show that incomplete training sets mainly affect the peripheral regions of self space instead of the whole of sample space. According to the analytical results, an Incremental Algorithm (IA) is proposed to update incomplete training sets dynamically. The experiment results demonstrate IA algorithm can update local self space having changed instead of the whole space incrementally and dynamically so that IAIDM can adjust itself to the current network environment quickly.

关键词： Intrusion detection system incompletion natural immune system

作者: FU-XIONG SUN

作者单位: Institute of Information, Zhongnan University of Economics and Law, Wuhan 430074, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2948-2953

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）