基于模糊逻辑和免疫遗传算法的入侵检测  

Intrusion Detection Based on Fuzzy Logic and Immune GA

作　　者：蔡伟鸿[1] 刘震[1] 王美林[1] 

机构地区：[1]汕头大学计算机科学与工程系,汕头515063

出　　处：《计算机工程》2006年第7期151-153,共3页Computer Engineering

基　　金：广东省科技攻关计划资助项目(2003C101015);广东省自然科学基金资助项目(04010981)

摘　　要：随着网络入侵方法和网络计算环境的变化,使得入侵越来越难以被检测和防范。该文论述了通过使用模糊数据挖掘和免疫遗传算法,分别对正常行为模式和待检测行为模式建立关联、序列规则集,进而通过比较待检测行为模式的规则集与正常行为模式的规则集的相似度,确定是否有入侵事件发生。经过仿真测试,证明该方法可以有效地检测异常攻击事件。The ever-rising complexity of intrusion methods and communication networks has resulted in increased difficulty in detecting intrusion. Using the fuzzy logic with data mining and immune genetic algorithm, this paper creates respectively the rule collection of natural behavior mode and inspecting behavior mode. Whether or not the intrusion is happened can be judged by the difference between the two rule collections. Experiment results indicate that the algorithm has good efficiency in identifying the abnormal intrusion.

关 键 词：入侵检测 模糊逻辑 免疫遗传算法 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]