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
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
ACM SIGCOMM Computer Communication Review
Net neutrality: the technical side of the debate: a white paper
ACM SIGCOMM Computer Communication Review
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Byte me: a case for byte accuracy in traffic classification
Proceedings of the 3rd annual ACM workshop on Mining network data
ACM SIGCOMM Computer Communication Review
Network monitoring using traffic dispersion graphs (tdgs)
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
TIE: A Community-Oriented Traffic Classification Platform
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
Graph-based P2P traffic classification at the internet backbone
INFOCOM'09 Proceedings of the 28th IEEE international conference on Computer Communications Workshops
Internet traffic classification demystified: on the sources of the discriminative power
Proceedings of the 6th International COnference
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
A parameterizable methodology for Internet traffic flow profiling
IEEE Journal on Selected Areas in Communications
Statistical traffic classification by boosting support vector machines
Proceedings of the 7th Latin American Networking Conference
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Application traffic classification at the early stage by characterizing application rounds
Information Sciences: an International Journal
Toward an efficient and scalable feature selection approach for internet traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
An information-theoretical approach to high-speed flow nature identification
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
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Recent research on Internet traffic classification has produced a number of approaches for distinguishing types of traffic. However, a rigorous comparison of such proposed algorithms still remains a challenge, since every proposal considers a different benchmark for its experimental evaluation. A lack of clear consensus on an objective and cientific way for comparing results has made researchers uncertain of fundamental as well as relative contributions and limitations of each proposal. In response to the growing necessity for an objective method of comparing traffic classifiers and to shed light on scientifically grounded traffic classification research, we introduce an Internet traffic classification benchmark tool, NeTraMark. Based on six design guidelines (Comparability, Reproducibility, Efficiency, Extensibility, Synergy, and Flexibility/Ease-of-use), NeTraMark is the first Internet traffic lassification benchmark where eleven different state-of-the-art traffic classifiers are integrated. NeTraMark allows researchers and practitioners to easily extend it with new classification algorithms and compare them with other built-in classifiers, in terms of three categories of performance metrics: per-whole-trace flow accuracy, per-application flow accuracy, and computational performance.