C4.5: programs for machine learning
C4.5: programs for machine learning
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
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
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
ACM SIGCOMM Computer Communication Review
Revealing skype traffic: when randomness plays with you
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Wide-scale botnet detection and characterization
HotBots'07 Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
Unconstrained endpoint profiling (googling the internet)
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
P4p: provider portal for applications
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Efficient application identification and the temporal and spatial stability of classification schema
Computer Networks: The International Journal of Computer and Telecommunications Networking
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Early recognition of encrypted applications
PAM'07 Proceedings of the 8th international conference on Passive and active network measurement
On the validation of traffic classification algorithms
PAM'08 Proceedings of the 9th international conference on Passive and active network measurement
A survey of techniques for internet traffic classification using machine learning
IEEE Communications Surveys & Tutorials
A first look at traffic classification in enterprise networks
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Bayesian classification: methodology for network traffic classification combination
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Graption: A graph-based P2P traffic classification framework for the internet backbone
Computer Networks: The International Journal of Computer and Telecommunications Networking
On profiling residential customers
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
Pytomo: a tool for analyzing playback quality of YouTube videos
Proceedings of the 23rd International Teletraffic Congress
A longitudinal view of HTTP video streaming performance
Proceedings of the 3rd Multimedia Systems Conference
Measuring the impact of the copyright amendment act on New Zealand residential DSL users
Proceedings of the 2012 ACM conference on Internet measurement conference
Analyzing the impact of YouTube delivery policies on user experience
Proceedings of the 24th International Teletraffic Congress
IEEE/ACM Transactions on Networking (TON)
Dissecting Bufferbloat: measurement and per-application breakdown of queueing delay
Proceedings of the 2013 workshop on Student workhop
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Accurate identification of network traffic according to application type is a key issue for most companies, including ISPs. For example, some companies might want to ban p2p traffic from their network while some ISPs might want to offer additional services based on the application. To classify applications on the fly, most companies rely on deep packet inspection (DPI) solutions. While DPI tools can be accurate, they require constant updates of their signatures database. Recently, several statistical traffic classification methods have been proposed. In this paper, we investigate the use of these methods for an ADSL provider managing many Points of Presence (PoPs). We demonstrate that statistical methods can offer performance similar to the ones of DPI tools when the classifier is trained for a specific site. It can also complement existing DPI techniques to mine traffic that the DPI solution failed to identify. However, we also demonstrate that, even if a statistical classifier is very accurate on one site, the resulting model cannot be applied directly to other locations. We show that this problem stems from the statistical classifier learning site specific information.