Abstract: 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.

Key words: Intrusion Detection, Support Vector Machine (SVM), Co-training, Small Sample, Classifier

摘要： 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。

关键词: 入侵检测, 支持向量机, 协作训练, 小样本, 分类器