Title :
Research on Intrusion Detection Method Based on SVM Co-training
Author :
Shuyue, Wu ; Jie, Yu ; Xiaoping, Fan
Author_Institution :
Central South Univ., Changsha, China
Abstract :
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.
Keywords :
computer network security; pattern classification; support vector machines; SVM cotraining based method; classifiers; network intrusion detection method; support vector machines; Accuracy; Classification algorithms; Intrusion detection; Prediction algorithms; Support vector machines; Training; Training data; Co-training; Intrusion Detection; SVM;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
Conference_Location :
Shenzhen, Guangdong
Print_ISBN :
978-1-61284-289-9
DOI :
10.1109/ICICTA.2011.452
