Research on Intrusion Detection Method Based on SVM Co-training

摘要

Currently, network intrusion detection is in face of the conflict between the difficult to label data and the high accuracy request to detect intrusion. In this paper, we propose a SVM co-training based method to detect network intrusion. It exploits the large amount of unlabeled data, and increase the detection accuracy and stability by co-training two classifiers. The simulation results show that our method is 11.9% more accurate than the traditional SVM method, and it depends less on the training dataset and test dataset.