支持向量机(support vector machine,SVM)是分类算法中集高效性、准确率和实时性于一体的分类方案。但由于在SVM分类决策的过程中,无关的分类器也参与了投票,使得方案的实时性和分类可靠性有一定程度的降低。提出了基于相似度的高效SVM...支持向量机(support vector machine,SVM)是分类算法中集高效性、准确率和实时性于一体的分类方案。但由于在SVM分类决策的过程中,无关的分类器也参与了投票,使得方案的实时性和分类可靠性有一定程度的降低。提出了基于相似度的高效SVM网络流量识别方案(efficient SVM based on similarity,ESVMS)。ESVMS通过估算待分类实例可能所属的类别范围,排除SVM中那些无关分类器的投票决策。实验结果表明ESVMS较SVM分类准确度几乎没有降低,但分类实时性进一步提高。展开更多
In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets w...In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification.展开更多
文摘支持向量机(support vector machine,SVM)是分类算法中集高效性、准确率和实时性于一体的分类方案。但由于在SVM分类决策的过程中,无关的分类器也参与了投票,使得方案的实时性和分类可靠性有一定程度的降低。提出了基于相似度的高效SVM网络流量识别方案(efficient SVM based on similarity,ESVMS)。ESVMS通过估算待分类实例可能所属的类别范围,排除SVM中那些无关分类器的投票决策。实验结果表明ESVMS较SVM分类准确度几乎没有降低,但分类实时性进一步提高。
基金The Program for New Century Excellent Talents in University(No.NCET-11-0565)the Fundamental Research Funds for the Central Universities(No.K13JB00160,2012JBZ010,2011JBM217)+2 种基金the Ph.D.Programs Foundation of Ministry of Education of China(No.20120009120010)the Program for Innovative Research Team in University of Ministry of Education of China(No.IRT201206)the Natural Science Foundation of Shandong Province(No.ZR2012FM010,ZR2011FZ001)
文摘In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification.
