基于改进NN-SVM算法的网络入侵检测

在网络入侵检测中,引入类归属度对NN-SVM算法进行改进.综合距离与同异类点个数因素,通过计算样本点对最近T个样本点的类别归属程度来决定取舍,以此对样本集进行修剪,从而降低正反类的混淆程度,以降低SVM的学习代价,提高泛化能力.试验表明:与SVM算法相比,改进的NN-SVM算法能有效地减少学习样本数,解决小样本的机器学习问题,提高系统检测性能.

Internet intrusion detection system based on improved NN-SVM

Introducing the Degree of Class Ownership would improve NN-SVM algorithm in internet intrusion detection.According to the distance and the number of the same class or the different class, calculating the degree of class ownership of the sample point to its T nearest neighbors decided whether the sample point should be reserved or deleted.Based on this,the improved NN-SVM algorithm pruned the training sample set to reduce the confusion degree of the positive and negative categories.As a result, it could effe...