Measurements and analysis of end-to-end Internet dynamics
Measurements and analysis of end-to-end Internet dynamics
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
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
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
Automated Traffic Classification and Application Identification using Machine Learning
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
ACM SIGCOMM Computer Communication Review
Traffic classification through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
Identifying and discriminating between web and peer-to-peer traffic in the network core
Proceedings of the 16th international conference on World Wide Web
Lightweight application classification for network management
Proceedings of the 2007 SIGCOMM workshop on Internet network management
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
Support Vector Machines for TCP traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
A survey of techniques for internet traffic classification using machine learning
IEEE Communications Surveys & Tutorials
Estimating routing symmetry on single links by passive flow measurements
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
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Statistical traffic classification techniques are often developed under the assumption that monitoring devices can observe the two half-flows composing each traffic session. However, the practice of asymmetric routing is rapidly moving from the Internet core to its edge. Forecasts [1] predict that in a few years even the last legs of Internet connectivity will experience some form of this practice. In this paper we study the effects that asymmetric routing can have on statistical traffic classifiers. We do so by comparing the capability of unidirectional classifiers with the ones of bidirectional classifiers in extracting information from the features of half-flows. Numerical results obtained by processing three heterogeneous traffic traces not only confirm the obvious assumption that bidirectional classifiers work better than unidirectional ones, but also shed some light on a few interesting facts. First, that the improvement introduced by bidirectional classifiers is not very significant in terms of increased true positives, while it is substantial in terms of decreased false positives. Furthermore, some protocols seem to exhibit, at least in some environments, the tendency to carry more information (relevant to traffic classification) in one direction than in the other.