【摘要】 计算机网络的发展也带来了网络安全威胁问题,采用网络入侵检测的方法能有效防范网络攻击。提出了基于互信息MI与支持向量机SVM算法的入侵检测方法。MI算法对高维的数据集进行特征选择,实现数据降维,提高入侵检测效率;SVM算法实现网络连接行为的分类,判别攻击行为。理论分析与实验结果表明,基于MI与SVM的方法能有效地提高检测效率并获得较高的分类准确率。更多还原

【Abstract】 The Cyber security threats have emerged with development of computer network. The method of network intrusion detection can effectively prevent network attack. An intrusion detection method based on support vector machine mutual information MI and SVM algorithm is proposed in this paper. MI algorithm can Execute transaction of the high dimensional dataset’s feature selection. The goal of MI algorithm is to reduce the dimension of the high dimensional dataset and improves the intrusion detection efficiency. The SVM algorithm realizes the classification of the network connection behavior and detects the attack behavior. The experimental results and theoretical analysis of the algorithms show that the method of intrusion detection based on MI and SVM can effectively improve the detection efficiency and obtain a higher classification accuracy.更多还原