The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
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
An Improved AdaBoost Algorithm Based on Adaptive Weight Adjusting
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Improving Svm Learning Accuracy with Adaboost
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 03
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
Support Vector Machines for TCP traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
GT: picking up the truth from the ground for internet traffic
ACM SIGCOMM Computer Communication Review
Early traffic classification using support vector machines
Proceedings of the 5th International Latin American Networking Conference
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
On the validation of traffic classification algorithms
PAM'08 Proceedings of the 9th international conference on Passive and active network measurement
NeTraMark: a network traffic classification benchmark
ACM SIGCOMM Computer Communication Review
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
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In recent years, traffic classification based on the statistical properties of flows has become an important topic. In this paper we statistically analyze the data length of the first few segments exchanged by a transport flow. This traffic classification method may be useful for early traffic identification in real time, since it takes into account only the beginning of the flow and therefore it can be used to trigger on-line actions. This work proposes the use of a supervised machine learning method for traffic identification based on Support Vector Machines (SVM). We compare the SVM classification accuracy with a more classical centroid based approach, obtaining good results. We also propose an improvement of the classification accuracy preformed by one single SVM model, introducing a weighted voting scheme of the verdicts of a sequence of SVM models. This sequence is generated by means of the boosting technique and the proposed method improves the classification accuracy of poorly classified classes without noticeable detriment of the other traffic classes. This work analyzes the behavior of both TCP and UDP transport protocols.