Empirically derived analytic models of wide-area TCP connections
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Traffic classification on the fly
ACM SIGCOMM Computer Communication Review
Traffic classification through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
Offline/realtime traffic classification using semi-supervised learning
Performance Evaluation
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
Timely and continuous machine-learning-based classification for interactive IP traffic
IEEE/ACM Transactions on Networking (TON)
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Supervised statistical approaches for the classification of network traffic are quickly moving from research laboratories to advanced prototypes, which in turn will become actual products in the next few years. While the research on the classification algorithms themselves has made quite significant progress in the recent past, few papers have examined the problem of determining the optimum working parameters for statistical classifiers in a straightforward and foolproof way. Without such optimization, it becomes very difficult to put into practice any classification algorithm for network traffic, no matter how advanced it may be. In this paper we present a simple but effective procedure for the optimization of the working parameters of a statistical network traffic classifier. We put the optimization procedure into practice, and examine its effects when the classifier is run in very different scenarios, ranging from medium and large local area networks to Internet backbone links. Experimental results show not only that an automatic optimization procedure like the one presented in this paper is necessary for the classifier to work at its best, but they also shed some light on some of the properties of the classification algorithm that deserve further study.