计算机应用与软件Issue(9)：187-190,4.DOI:10.3969/j.issn.1000-386x.2013.09.052
基于 IRBF 的入侵检测系统的研究
RESEARCH ON IRBF-BASED INTRUSION DETECTION SYSTEM
摘要
Abstract
As an active and dynamic networks security-defense technique , intrusion detection can resist the attacks from inside and outside the networks , and plays an important role in assuring the networks security .We study a learning algorithm which applies the clonal selection principle-based immune recognition algorithm to radial basis function ( RBF) neural network .This algorithm uses input data as the antigens and antibodies as the hidden layer centres of RBF neural network , adopts recursive least square method to determine the weights , improves the convergence speed and precision of RBF neural network .This algorithm has been successfully applied to the intrusion detection systems . Theory and experiment show that this algorithm has better ability in intrusion detection , and can be used to improve the efficiency of intrusion detection, reduce the false alarm rate .关键词
入侵检测/径向基函数神经网络/克隆选择/免疫算法Key words
Intrusion detection/Radial basis function neural network/Clonal selection/Immune algorithm分类
信息技术与安全科学引用本文复制引用
彭义春,牛熠,胡琦伟..基于 IRBF 的入侵检测系统的研究[J].计算机应用与软件,2013,(9):187-190,4.基金项目
广东省科技计划项目（2009B010800042）。 （）
