User menu

Accès à distance ? S'identifier sur le proxy UCLouvain

GTVS: Boosting the Collection of Application Traffic Ground Truth

Primary tabs

Canini, Marco [UCL] Li, Wei [University of Cambridge] Moore, Andrew W. [University of Cambridge] Bolla, Raffaele [University of Genoa]

Interesting research in the areas of traffic classification, network monitoring, and application-oriented analysis can not proceed without real traffic traces, labeled with actual application information. However, hand-labeled traces are an extremely valuable but scarce resource in the traffic monitoring and analysis community, as a result of both privacy concerns and technical difficulties. Hardly any possibility exists for payloaded data to be released, while the impossibility of obtaining certain ground-truth application information from non-payloaded data has severely constrained the value of anonymized public traces. The usual way to obtain the ground truth is fragile, inefficient and not directly comparable from one's work to another. This paper proposes a methodology and details the design of a technical framework that significantly boosts the efficiency in compiling the application traffic ground truth. Further, a case study on a 30 minute real data trace is presented. In contrast with past work, this is an easy hands-on tool suite dedicated to save user's time and labor and is freely available to the public.

Document type Communication à un colloque (Conference Paper) – Présentation orale avec comité de sélection
Access type Accès interdit
Publication date 2009
Language Anglais
Conference "TMA '09"
Peer reviewed yes
Host document "Proceedings of the First International Workshop on Traffic Monitoring and Analysis" (ISBN : 978-3-642-01644-8)
Publication status Publié
Affiliation UCL - SST/ICTM/INGI - Pôle en ingénierie informatique
Keywords 1162
Links
  1. Moore Andrew W., Papagiannaki Konstantina, Toward the Accurate Identification of Network Applications, Lecture Notes in Computer Science (2005) ISBN:9783540255208 p.41-54, 10.1007/978-3-540-31966-5_4
  2. Moore Andrew W., Zuev Denis, Internet traffic classification using bayesian analysis techniques, 10.1145/1064212.1064220
  3. Karagiannis Thomas, Papagiannaki Konstantina, Faloutsos Michalis, BLINC : multilevel traffic classification in the dark, 10.1145/1080091.1080119
  4. Erman Jeffrey, Arlitt Martin, Mahanti Anirban, Traffic classification using clustering algorithms, 10.1145/1162678.1162679
  5. Dusi M., Crotti M., Gringoli F., Salgarelli L., Tunnel Hunter: Detecting application-layer tunnels with statistical fingerprinting, 10.1016/j.comnet.2008.09.010
  6. Li W., Moore A. W., A Machine Learning Approach for Efficient Traffic Classification, 10.1109/mascots.2007.2
  7. Karagiannis, T., Broido, A., Faloutsos, M., Claffy, K.: Transport layer identification of P2P traffic. In: Proceedings of Internet Measurement Conference (2004)
  8. Trestian Ionut, Ranjan Supranamaya, Kuzmanovi Aleksandar, Nucci Antonio, Unconstrained endpoint profiling (googling the internet), 10.1145/1402958.1402991
  9. Dreger, H., et al.: Dynamic application-layer protocol analysis for network intrusion detection. In: 15th USENIX Security Symposium (2006)
  10. Szabó Géza, Orincsay Dániel, Malomsoky Szabolcs, Szabó István, On the Validation of Traffic Classification Algorithms, Lecture Notes in Computer Science ISBN:9783540792314 p.72-81, 10.1007/978-3-540-79232-1_8
Bibliographic reference Canini, Marco ; Li, Wei ; Moore, Andrew W. ; Bolla, Raffaele. GTVS: Boosting the Collection of Application Traffic Ground Truth.TMA '09In: Proceedings of the First International Workshop on Traffic Monitoring and Analysis, 2009
Permanent URL http://hdl.handle.net/2078.1/139438