关键词: 入侵检测, 支持向量机, 协作训练, 小样本, 分类器

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