摘要
随着入侵检测技术(IDS)在网络安全领域的作用越来越重要,将多种软计算方法应用到入侵检测技术中是构建智能入侵检测系统的新途径和尝试。本文将模糊数据挖掘技术和遗传算法相结合,提出一种基于遗传算法的模糊规则反复学习的方法,构造具有自适应能力的分类器,并进一步应用到计算机网络的入侵检测中。仿真测试证明了该方法的有效性。
Intrusion detection techniques become increasingly important in the area of network security. It is a novel attempt that various soft computing approaches are applied to the intrusion detection field. This paper combines the fuzzy data mining technology and genetic algorithms, describes a fuzzy genetic-based learning algorithm, constructs a fuzzy rule classifier and discusses its usage to further detect intrusion in a computer network. Experimental results indicate the efficiency of the algorithm.
出处
《计算机工程与科学》
CSCD
北大核心
2009年第8期36-38,共3页
Computer Engineering & Science
基金
国家自然科学基金资助项目(60874113)
福建省教育厅科技基金资助项目(JA05300)
关键词
入侵检测
模糊逻辑
遗传算法
分类器
intrusion detection
fuzzy logic
genetic algorithm
classifier
