基于加权特征筛选的入侵检测系统

摘要

网络攻击隐蔽性高,手段多样.传统检测系统特征提取不全,数据包易丢失,漏报、错报率高.为提高检测率,提出一种基于加权特征筛选的入侵检测算法.首先对网络数据包进行特征提取；然后采用支持向量机交叉验证对全部特征进行筛选,并计算各特征的权值；最后以加权保留特征构建入侵检测模型.仿真实例结果表明,该检测算法提高了入侵检测率,是一种有效的网络入侵检测方法.%The network intrusion means are diversification. Traditional detection system can not extract feature very well. Packet is easy lost,and omission and misstatement rates are high. In order to improve the detection rate, this paper proposed an intrusion detection algorithm based on weighted feature selection. Firstly, features were extracted from network packets, then support vector machine (SVM) was used to select feature based on cross validation and calculate the feature values,lastly,intrusion detection mode was set up based on the weighted reserves features. The results of simulation experiment show that the proposed algorithm improves the intrusion detection rate. It is an effective network intrusion detection method.