A modular machine learning system for flow-level traffic classification in large networks

ACM Transactions on Knowledge Discovery from Data

Volumn 6, Issue 1, 2012, Pages

  Jin, Yu ^a,b   Duffield, Nick ^b   Erman, Jeffrey ^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

Algorithms; Measurement; Theory

Indexed keywords

APPLICATION PROTOCOLS; DATA SETS; DISPLAY PERFORMANCE; ERROR RATE; ERROR RATE REDUCTION; FLOW DATA; FLOW-LEVEL; GLOBAL ENTERPRISE NETWORKS; INTELLIGENT DATA; LARGE NETWORKS; LINEAR BINARY CLASSIFIERS; MANAGEMENT TASKS; MODULAR ARCHITECTURES; MODULAR MACHINES; NETWORK TRAFFIC; PACKET LEVEL; REAL TRAFFIC; RUNTIMES; STABILITY REQUIREMENTS; TCP FLOWS; THEORY; TRAFFIC CLASS; TRAFFIC CLASSIFICATION; TRAFFIC STATISTICS; TWO-STEP MODEL; UDP FLOWS;

ALGORITHMS; LEARNING SYSTEMS; MEASUREMENTS; SCALABILITY; TRAFFIC SURVEYS; TRANSMISSION CONTROL PROTOCOL;

TELECOMMUNICATION TRAFFIC;

EID: 84859393329     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/2133360.2133364     Document Type: Article

References (35)

1. BERNAILLE, L., TEIXEIRA, R., AND SALAMATIAN, K. 2006. Early application identification. In Proceedings of CoNext'06. ACM.
2. BUT, J., NGUYEN, T., STEWART, L., WILLIAMS, N., AND ARMITAGE, G. 2007. Performance analysis of the angel system for automated control of game traffic prioritisation. In Proceedings of the 6th ACM SIGCOMM Workshop on Network and System Support For Games (NetGames). 123-128.
3. CHEN, A., JIN, Y., CAO, J., AND LI, L. 2010. Tracking long duration flows in network traffic. In Proceedings of the 29th Conference on Information Communications (INFOCOM). 206-210.
4. CROTTI, M., DUSI, M., GRINGOLI, F., AND SALGARELLI, L. 2007. Traffic classification through simple statistical fingerprinting. ACM SIGCOMM Comput. Comm. Rev. 37, 1, 5-16.
5. DUDA, R. O.,HART, P. E., AND STORK, D. G. 2000. Pattern Classification. Wiley-Interscience.
6. ERMAN, J.,MAHANTI, A., ARLITT, M. F., COHEN, I., AND WILLIAMSON, C. L. 2007. Offline/realtime traffic classification using semi-supervised learning. Perform. Eval. 64, 9-12, 1194-1213. (Pubitemid 47302132)
7. FREUND, Y. AND SCHAPIRE, R. E. 1995. A decision-theoretic generalization of on-line learning and an application to boosting. In Proceedings of the 2nd European Conference on Computational Learning Theory (EuroCOLT).
8. GALLAGHER, B. AND ELIASSI-RAD, T. 2007. An examination of experimental methodology for classifiers of relational data. In Proceedings of the 7th IEEE International Conference on Data Mining Workshops (ICDMW). IEEE Computer Society Press, 411-416.
9. HAFFNER, P., SEN, S., SPATSCHECK, O., AND WANG, D. 2005. ACAS: Automated Construction of Application Signatures. In Proceedings of the SIGCOMMWorkshop on Mining Network Data (MineNet). ACM.
10. ILIOFOTOU, M., PAPPU, P., FALOUTSOS, M., MITZENMACHER, M., SINGH, S., AND VARGHESE, G. 2007. Network monitoring using traffic dispersion graphs (TDGS). In Proceedings of the ACM Internet Measurement Conference (IMC).
11. ILIOFOTOU, M., FALOUTSOS, M., AND MITZENMACHER, M. 2009a. Exploiting dynamicity in graph-based traffic analysis: techniques and applications. In Proceedings of CoNext'09. ACM.
12. ILIOFOTOU, M., KIM, H., FALOUTSOS, M., MITZENMACHER, M., PAPPU, P., AND VARGHESE, G. 2009b. Graph-based p2p traffic classification at the internet backbone. In Proceedings of the 28th IEEE International Conference on Computer Communications Workshops (INFOCOM). 37-42.
13. JACOBS, R. A., JORDAN, M. I.,NOWLAN, S. J., AND HINTON, G. E. 1991. Adaptive mixture of local experts. Neural Computat. 3, 79-87.
14. JIANG, H., MOORE, A. W., GE, Z., JIN, S., AND WANG, J. 2007. Lightweight application classification for network management. In Proceedings of the 2007 SIGCOMM Workshop on Internet Network Management (INM). ACM.
15. JIN, Y., SHARAFUDDIN, E., AND ZHANG, Z.-L. 2009. Unveiling core network-wide communication patterns through application traffic activity graph decomposition. In Proceedings of SIGMETRICS'09. 49-60.
16. JIN, Y., DUFFIELD, N., HAFFNER, P., SEN, S., AND ZHANG, Z.-L. 2010a. Inferring applications at the network layer using collective traffic statistics. In Proceedings of the 22nd International Teletraffic Congress (ITC'22).
17. JIN, Y., DUFFIELD, N., HAFFNER, P., SEN, S., AND ZHANG, Z.-L. 2010b. Inferring applications at the network layer using collective traffic statistics (extended abstract). In Proceedings of ACM SIGMETRICS' 10.
18. KARAGIANNIS, T., BROIDO, A., FALOUTSOS, M., AND CLAFFY, K. 2004. Transport layer identification of P2P traffic. In Proceedings of the ACM Internet Measurement Conference (IMC).
19. KARAGIANNIS, T., PAPAGIANNAKI, K., AND FALOUTSOS, M. 2005. BLINC: Multilevel traffic classification in the dark. In Proceedings of ACM SIGCOMM'05. ACM.
20. MA, J., LEVCHENKO, K., KREIBICH, C., SAVAGE, S., AND VOELKER, G. M. 2006. Unexpected means of protocol inference. In Proceedings of the ACM Internet Measurement Conference (IMC).
21. MCDANIEL, P., SEN, S., SPATSCHECK, O., DER MERWE, J. V., AIELLO, B., AND KALMANEK, C. 2006. Enterprise security: A community of interest based approach. In Proceedings of the 13th Annual Network and Distributed System Security Symposium (NDSS).
22. MOORE, A. W. AND ZUEV, D. 2005. Internet traffic classification using Bayesian analysis techniques. In Proceedings of ACM SIGMETRICS'05.
23. NEVILLE, J. AND JENSEN, D. 2000. Iterative classification in relational data. In Proceedings of the AAAI Workshop on Learning Statistical Models from Relational Data. AAAI.
24. NGUYEN, T. AND ARMITAGE, G. 2006a. Synthetic sub-flow pairs for timely and stable IP traffic identification. In Proceedings of the Australian Telecommunication Networks and Application Conference. 293-297.
25. NGUYEN, T. AND ARMITAGE, G. 2006b. Training on multiple sub-flows to optimise the use of machine learning classifiers in real-world IP networks. In Proceedings of the 31st Conference on Local Computer Networks. IEEE.
26. PHILLIPS, S. J., DUDÍK, M., AND SCHAPIRE, R. E. 2004. A maximum entropy approach to species distribution modeling. In Proceedings of the 21st International Conference on Machine Learning (ICML). ACM.
27. PLATT, J. 1999. Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In Proceedings of the 13th Conference on Neural Information Processing Systems (NIPS).
28. RIFKIN, R. AND KLAUTAU, A. 2004. In defense of one-vs-all classification. J. Mach. Learn. Res., 101-141.
29. SCHAPIRE, R. E. AND SINGER, Y. 2000. Boostexter: A boosting-based system for text categorization. Mach. Learn. 39, 2-3, 135-168. (Pubitemid 30594821)
30. SEN, S., SPATSCHECK, O., AND WANG, D. 2004. Accurate, scalable in-network identification of P2P traffic using application signatures. In Proceedings of the 13th International World Wide Web Conference (WWW). ACM.
31. SEN, P., NAMATA, G., BILGIC, M., GETOOR, L., GALLAGHER, B., AND ELIASSI-RAD, T. 2008. Collective classification in network data. AI Mag. 29, 3.
32. TRESTIAN, I., RANJAN, S., KUZMANOVI, A., AND NUCCI, A. 2008. Unconstrained endpoint profiling (Googling the Internet). In Proceedings of ACM SIGCOMM '08.
33. WILLIAMS, N., ZANDER, S., AND ARMITAGE, G. 2006. A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. SIGCOMM Comput. Comm. Rev. 36, 5-16.
34. WITTEN, I. H. AND FRANK, E. 1999. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann.
35. XU, K., ZHANG, Z.-L., AND BHATTACHARYYA, S. 2005. Profiling internet backbone traffic: Behavior models and applications. In Proceedings of ACM SIGCOMM.