Transport layer identification of P2P traffic
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
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
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
ACM SIGCOMM Computer Communication Review
NetFPGA--An Open Platform for Gigabit-Rate Network Switching and Routing
MSE '07 Proceedings of the 2007 IEEE International Conference on Microelectronic Systems Education
Network utility maximization for triple-play services
Computer Communications
NetFPGA: reusable router architecture for experimental research
Proceedings of the ACM workshop on Programmable routers for extensible services of tomorrow
A Machine Learning Approach for Efficient Traffic Classification
MASCOTS '07 Proceedings of the 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Machine Learned Real-Time Traffic Classifiers
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 03
Inferring undesirable behavior from P2P traffic analysis
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
A Dynamic Online Traffic Classification Methodology Based on Data Stream Mining
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 01
GT: picking up the truth from the ground for internet traffic
ACM SIGCOMM Computer Communication Review
Machine Learning Techniques for Feature Reduction in Intrusion Detection Systems: A Comparison
ICCIT '09 Proceedings of the 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology
An SVM-based machine learning method for accurate internet traffic classification
Information Systems Frontiers
Proceedings of the ACM SIGCOMM 2010 conference
Experience with high-speed automated application-identification for network-management
Proceedings of the 5th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
Real-Time Classification of Multimedia Traffic Using FPGA
FPL '10 Proceedings of the 2010 International Conference on Field Programmable Logic and Applications
Realtime classification for encrypted traffic
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
A compact 3D VLSI classifier using bagging threshold network ensembles
IEEE Transactions on Neural Networks
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Classifying online network traffic is becoming critical in network management and security. Recently, new classification methods based on analysis of statistical features of transport layer traffic have been proposed. While these new methods address the limitations of the port based and payload based traffic classification, the current software-based solutions are not fast enough to deal with the traffic of today's high-speed networks. In this paper, we propose an online statistical traffic classifier using the C4.5 machine learning algorithm running on the NetFPGA platform. Our NetFPGA classifier is constructed by adding three main modules to the NetFPGA reference switch design; a Netflow module, a feature extractor module, and a C4.5 search tree classifier. The proposed classifier is able to classify the input traffics at the maximum line speed of the NetFPGA platform, i.e. 8Gbps without any packet loss. Our method is based on the statistical features of the first few packets of a flow. The flow is classified just a few micro seconds after receiving the desired number of packets.