The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
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
Traffic classification on the fly
ACM SIGCOMM Computer Communication Review
Unexpected means of protocol inference
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Traffic classification through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Early classification of network traffic through multi-classification
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
An aggressive margin-based algorithm for incremental learning
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Statistical traffic classification by boosting support vector machines
Proceedings of the 7th Latin American Networking Conference
Timely and continuous machine-learning-based classification for interactive IP traffic
IEEE/ACM Transactions on Networking (TON)
Detection and classification of peer-to-peer traffic: A survey
ACM Computing Surveys (CSUR)
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Internet traffic classification is an essential task for managing large networks. Network design, routing optimization, quality of service management, anomaly and intrusion detection tasks can be improved with a good knowledge of the traffic. Traditional classification methods based on transport port analysis have become inappropriate for modern applications. Payload based analysis using pattern searching have privacy concerns and are usually slow and expensive in computational cost. In recent years, traffic classification based on the statistical properties of flows has become a relevant topic. In this work we analyze the size of the firsts packets on both directions of a flow as a relevant statistical fingerprint. This fingerprint is enough for accurate traffic classification and so can be useful for early traffic identification in real time. This work proposes the use of a supervised machine learning clustering method for traffic classification based on Support Vector Machines. We compare our method accuracy with a more classical centroid based approach, obtaining promising results.