摘要
针对基于人工免疫的入侵检测技术中所使用的传统反向选择算法,在面对大量的网络通信数据或具有多个分离特征区间网络通信数据时的无效性,提出了基于模糊控制及遗传算法的反向选择算法。在利用反向选择算法生成抗体时,首先利用模糊控制原理来确定抗体的数量,使得计算机中抗体的数量处于最优,然后为了达到在一定数量抗体时种群的总体免疫力最大,引入了遗传算法来进化种群,最终使得在计算机中抗体的数量得到控制,同时在该数量下种群具有最大的免疫力。
In view of the ineffectivity of the traditional negative selection algorithm of the intrusion detection system based on the artifical immune system when it faces the massive network correspondence data or the data have many separation characteristic sectors, the nega- tive selection algorithm based on the fuzzy control theory and genetic algorithm is proposed. Firstly, the fuzzy control theory is employed to control the immune body quantity when using the negative selection algorithm to get immune body, so that the immune body＇s quantity is optimum in the computer. Then, the genetic algorithm is introduced to evolve the population in order to maximize the immunity when the quantity of immune body is definite. Accordingly, the quantity of the immune body is under controlled and the immunity of the popu- lation is superior under this quantity.
出处
《计算机工程与设计》
CSCD
北大核心
2007年第21期5193-5194,5288,共3页
Computer Engineering and Design
基金
湖北省教育厅重点科研基金项目(2004D006)。
关键词
人工免疫
入侵检测
反向选择
遗传算法
模糊控制
artificial immune
intrusion detection systems
negative selection algorithm
genetic algorithms
fuzzy control
