Introduction to Algorithms
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Transport layer identification of P2P traffic
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
ACAS: automated construction of application signatures
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Automated Traffic Classification and Application Identification using Machine Learning
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Traffic classification on the fly
ACM SIGCOMM Computer Communication Review
A Longitudinal Study of P2P Traffic Classification
MASCOTS '06 Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation
Identifying Known and Unknown Peer-to-Peer Traffic
NCA '06 Proceedings of the Fifth IEEE International Symposium on Network Computing and Applications
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
GA-Based Internet Traffic Classification Technique for QoS Provisioning
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
Offline/realtime traffic classification using semi-supervised learning
Performance Evaluation
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
Improving matching performance of DPI traffic classifier
Proceedings of the 2011 ACM Symposium on Applied Computing
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Accurate and real-time classifi cation of network traffic is significant to a number of network operation and management tasks such as quality of service differentiation, traffic shaping and security surveillance. However, with emerging P2P applications using dynamic port numbers, IP masquerading techniques and payload encryption, accurate and intelligent traffic classification continues to be a big challenge despite a wide range of research work on the topic. Since each classification method has its disadvantages and hardly could meet the specific requirement of Internet traffic classification, this paper innovatively presents a composite traffic classification system. The proposed lightweight system can accurately and effectively identify Internet traffic with good scalability to accommodate both known and unknown/encrypted applications. Furthermore, It promises to satisfy various Internet uses and is feasible for use in real-time line speed applications. Our experimental results show the distinct advantages of the proposed classification system.