基于改进SVM协作训练的入侵检测方法

Improved SVM co-training based intrusion detection

提出了在少量样本条件下,采用带变异因子的支持向量机(SVM)协作训练模型进行入侵检测的方法。充分利用大量未标记数据,通过两个分类器检测结果之间的迭代训练,可以提高检测算法的准确度和稳定性。在协作训练的多次迭代之间引入变异因子,减小由于过学习而降低训练效果的可能。仿真实验表明,该方法的检测准确度比传统的SVM算法提高了7.72%,并且对于训练数据集和测试数据集的依赖程度都较低。

In this paper,a Support Vector Machine（SVM） co-training based method with variation factors to detect network intrusion was proposed.It made full use of the large amount of unlabeled data,and increased the detection accuracy and stability by co-training two classifiers.It further introduced variation factors among multiple iterations to decrease the possibility of effect reduction due to over-learning.The simulation results show that the proposed method is 7.72% more accurate than the traditional SVM method,and it depends less on the training dataset and test dataset.