基于支持向量机与遗传算法的网络入侵检测的应用

摘要

入侵检测实质上是一个分类的问题,对于提高分类精度是十分重要的.支持向量机(SVM)是一个功能强人的用于解决分类问题的工具.基于支持向量机的入侵检测精度较高,但如何获得更高的精度是一个新的问题.本文利用基于支持向量机和遗传算法(GA)的入侵检测来解决这些问题.我们首先利用遗传算法进行特征选择及优化,然后使用支持向量机模型来检测入侵行为.为了验证我们的方法,我们利用(KDD)Cup99数据集测试并分析它的性能.实验结果表明,本文提出的方法是一种有效的网络入侵检测方法.%Intrusion detection is essentially a classification problem.It is very important to increase the classification accuracy.Support Vector Machine (SVM) is a powerful tool to solve classification problems.Intrusion detection based on SVM accuracy is relatively high, however how to get a higher accuracy is a new problem.In this paper, SVM and Genetic Algorithm (GA) are applied to intrusion detection to solve this problem.First the GA for feature selection and optimization is used, and then the SVM model is used to detect intrusions.In order to verify this approach, proposal with KDDCup99 dataset is tested, and its performance is analyzed.The experimental results show that the proposed approach is an efficient way in network intrusion detection.