An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
ACM SIGCOMM Computer Communication Review
Early application identification
CoNEXT '06 Proceedings of the 2006 ACM CoNEXT conference
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
Accurate, Fine-Grained Classification of P2P-TV Applications by Simply Counting Packets
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
KISS: Stochastic Packet Inspection
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
Graph-based P2P traffic classification at the internet backbone
INFOCOM'09 Proceedings of the 28th IEEE international conference on Computer Communications Workshops
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
A survey of techniques for internet traffic classification using machine learning
IEEE Communications Surveys & Tutorials
A Measurement Study of a Large-Scale P2P IPTV System
IEEE Transactions on Multimedia
Fine-grained traffic classification with netflow data
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Reviewing traffic classification
DataTraffic Monitoring and Analysis
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In the last few years the research community has proposed several techniques for network traffic classification. While the performance of these methods is promising especially for specific classes of traffic and particular network conditions, the lack of accurate comparisons among them makes it difficult to choose between them and find the most suitable technique for given needs. Motivated also by the increase of P2P-TV traffic, this work compares Abacus, a novel behavioral classification algorithm specific for P2P-TV traffic, and Kiss, an extremely accurate statistical payload-based classifier. We first evaluate their performance on a common set of traces and later we analyze their requirements in terms of both memory occupation and CPU consumption. Our results show that the behavioral classifier can be as accurate as the payload-based with also a substantial gain in terms of computational cost, although it can deal only with a very specific type of traffic.