TCFOM: A robust traffic classification framework based on OC-SVM combined with MC-SVM
New application traffic occurring on Internet frequently challenges the traditional traffic classifiers based on machine learning. These classifiers always identify it inaccurately and assign it into one of their known classes forcibly, even though the extra class is labeled as ‘other’ when training. In this case, the precision of identifying known classes is reduced. In this paper, a robust traffic classification framework based on OC-SVM combined with MC-SVM (TCFOM) is presented. We capture several kinds of application traffic, and carry out an experiment under supervised environment. Using the OC-SVM, the unknown traffic is classified into extra class labeled as ‘other’. The precision of identifying known traffic is improved. Using the unknown traffic identified, the new classifying model is set up.TCFOM can classify the unknown traffic and extend well. We compare TCFOM with three classifiers respectively based on SVM, RBF network, Na?ve Bayes. Experimental results show that the robustness of TCFOM is best.
TCFOM traffic classification OC-SVM MCSVM robust
Gang Lu Hongli Zhang Xuefu Sha Cheng Chen Lizhi Peng
State Key Lab of Computer Information Content Security,Department of Computer Science, Harbin Institute of Technology, Harbin, China
国际会议
南宁
英文
180-186
2010-10-13（万方平台首次上网日期，不代表论文的发表时间）