计算机应用与软件Issue(3)：301-303,333,4.DOI:10.3969/j.issn.1000-386x.2014.03.079
遗传算法同步选择特征和支持向量机参数的网络入侵检测
NETWORK INTRUSION DETECTION WITH GENETIC ALGORITHM SYNCHRONOUS SELECTING FEATURE AND SVM PARAMETERS
摘要
Abstract
Aiming at the high-dimensional data generated by intrusion detection systems and SVM parameter optimization problems,the paper puts forward a network intrusion detection model with genetic algorithm synchronous selecting feature and SVMparameters.At first the feature subsets and SVMparameters are coded as chromosome,and the network intrusion detection categorized accuracy is taken as grouped individual fitness degree value.Then depending on the global search ability of the genetic algorithm,it synchronously finds out feature combi-nations that most influence the categorization algorithm and the SVMoptimal parameters.At last it uses KDD99 datasets to carry out simula-tion experiments.Results show that the model can quickly find the optimal feature subset and SVMparameters and the network intrusion de-tection accuracy ratio is improved,so that it is regarded as a good network intrusion detection algorithm.关键词
特征选择/入侵检测/遗传算法/支持向量机Key words
Feature selction/Intrusion detection/Genetic algorithm/SVM分类
信息技术与安全科学引用本文复制引用
李学峰..遗传算法同步选择特征和支持向量机参数的网络入侵检测[J].计算机应用与软件,2014,(3):301-303,333,4.基金项目
2012年度青海省社会科学规划项目（批准号12026）。 （）
