Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
ACM SIGCOMM Computer Communication Review
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
Graph-based P2P traffic classification at the internet backbone
INFOCOM'09 Proceedings of the 28th IEEE international conference on Computer Communications Workshops
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
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Recently, with the progress of research on accurate traffic classification (TC), the major obstacle to achieving accurate TC is the lack of an efficient ground truth (GT) generation method. A firm GT is important for exploring the underlying characteristics of network traffic, building the traffic model, and verifying the classification result, etc. However, current existing GT generation methods can only be made manually or with additional high-cost DPI (deep packet inspection) devices. They are neither too complicated nor too expensive for research community. In response to this problem, we present LCGT, a lowcost continuous GT generation method for TC. Based on LCGT, we propose a novel updateable TC system, which can always reflect the features of up-to-date traffic. While we have found LCGT to be very useful in our own research, we seek to initiate a broader discussion to guide the refinement of the tools. LCGT is located on: http://code.google.com/p/traclassy