The nature of statistical learning theory
The nature of statistical learning theory
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Machine learning and data mining
Communications of the ACM
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
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
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
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
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Offline/realtime traffic classification using semi-supervised learning
Performance Evaluation
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
Semi-Supervised Learning
Real-Time Classification of Multimedia Traffic Using FPGA
FPL '10 Proceedings of the 2010 International Conference on Field Programmable Logic and Applications
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Network traffic classification is extremely important in numerous network functions today. However, most of the current approaches based on port number or payload detection are becoming increasingly impractical with the appearance of dynamic or encrypted applications. Even though some supervised learning based work were proposed, it is difficult to collect sufficient flow-labeled traces for training. On the other hand, online classification needs an early identification, which is still challenging for most well-known approaches. In this paper, we propose a semi-supervised learning based traffic classification approach named SMILER, which supports an early classification from the sizes of the first few packets (empirically 5 packets) of a flow. Experiments in real networks demonstrate that SMILER achieves 94% precision and 96% recall on average for all tested applications, even with disordered packets SMILER still works well. With a hybrid scheme, the performance is further improved. Meanwhile, SMILER performs fast in both classification and updating. All experimental results show that SMILER is practical for fast and accurate online traffic classification.