Inferring applications at the network layer using collective traffic statistics

Performance Evaluation Review

Volumn 38, Issue 1 SPEC. ISSUE, 2010, Pages 351-352

  Jin, Yu ^a   Duffield, Nick ^b   Haffner, Patrick ^b   Sen, Subhabrata ^b   Zhang, Zhi Li ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

^b AT AND T LABS RESEARCH   (United States)

Author keywords

Design; Measurement

Indexed keywords

AGGREGATE TRAFFIC; GRAPH-BASED; NOVEL TECHNIQUES; REAL TRAFFIC; SPATIAL DISTRIBUTION; TRAFFIC DISTRIBUTIONS; TRAFFIC FEATURES; TRAFFIC FLOW; TRAFFIC STATISTICS;

AGGREGATES; CALIBRATION; NETWORK LAYERS; SIZE DISTRIBUTION; TRAFFIC SURVEYS;

FLOW MEASUREMENT;

EID: 77954891495     PISSN: 01635999     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1811099.1811082     Document Type: Conference Paper

References (5)

1. T. Karagiannis, K. Papagiannaki and M. Faloutsos. BLINC: Multilevel traffic classification in the dark. In Proc. of ACM SIGCOMM, August 2005.
2. Andrew W. Moore and Denis Zuev. Internet traffic classification using bayesian analysis techniques. In Proc. of ACM SIGMETRICS'05, Banff, Canada, 2005.
3. Y. Jin, E. Sharafuddin, and Z-L. Zhang. Unveiling core network-wide communication patterns through application traffic activity graph decomposition. In Proc. of SIGMETRICS '09, pages 49-60, 2009.
4. Y. Jin, N. Duffield, P. Haffner, S. Sen, and Z.-L. Zhang.A modularmachine learning system for flow-level traffic classification in large networks. Technical report, 2010. http://www-users.cs.umn.edu/~yjin/papers/sigmetrics. tech.pdf.
5. P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3), 2008.