A novel semi-supervised approach for network traffic clustering

Proceedings - 2011 5th International Conference on Network and System Security, NSS 2011

Volumn , Issue , 2011, Pages 169-175

  Wang, Yu ^a   Xiang, Yang ^a   Zhang, Jun ^a   Yu, Shunzheng ^b  

^a DEAKIN UNIVERSITY   (Australia)

^b SUN YAT SEN UNIVERSITY   (China)

Author keywords

constrained clustering; constraints; machine learning; semi supervised learning; traffic classification

Indexed keywords

ALTERNATIVE APPROACH; APPLICATION PROTOCOLS; BACKGROUND INFORMATION; CONSTRAINED CLUSTERING; CONSTRAINTS; ESSENTIAL COMPONENT; FLOW LEVEL; K-MEANS ALGORITHM; MACHINE LEARNING TECHNIQUES; METRIC LEARNING; NETWORK DOMAINS; NETWORK TRAFFIC; NETWORK TRAFFIC CLASSIFICATION; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SEMI-SUPERVISED LEARNING METHODS; SOFT CONSTRAINT SATISFACTION; TRAFFIC CLASSIFICATION; TRAFFIC FLOW; TRAFFIC TRACES;

CONSTRAINT THEORY; LEARNING ALGORITHMS; LEARNING SYSTEMS; NETWORK MANAGEMENT; NETWORK SECURITY; SUPERVISED LEARNING; TEACHING; TELECOMMUNICATION TRAFFIC;

CLUSTERING ALGORITHMS;

EID: 81155123377     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNSS.2011.6059997     Document Type: Article

References (23)

1. M. Roughan, S. Sen, O. Spatscheck, and N. Duffield, "Class-of- service mapping for QoS: A statistical signature-based approach to IP traffic classification", in Proc. ACM/SIGCOMM Internet Measurement Conference (IMC) 2004, Sicily, Italy, October 2004.
2. A. Moore and D. Zuev, "Internet traffic classification using Bayesian analysis techniques", in ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), Alberta, Canada, June 2005.
3. T. Auld, A. W. Moore, and S. F. Gull, "Bayesian neural networks for Internet traffic classification", IEEE Trans. Neural Networks, no. 1, ppp. 223-239, January 2007.
4. T. Nguyen and G. Armitage, "Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networks", in Proc. IEEE 31st Conference on Local Computer Networks (LCN'06), Tampa, Florida, USA, November 2006.
5. M. Crotti, M. Dusi, F. Gringoli, and L. Salgarelli, "Traffic classification through simple statistical fingerprinting", SIGCOMM Comput. Commun. Rev., vol. 37, no. 1, pp. 5-16, 2007.
6. D. Schatzmann, W. Muhlbauer, T. Spyropoulos, and X. Dimitropoulos, "Digging into HTTPS: flow-based classification of webmail traffic", in Proceedings of the 10th annual conference on Internet measurement (IMC'10). ACM, New York, NY, USA, 322-327.
7. A. McGregor, M. Hall, P. Lorier, and J. Brunskill, "Flow clustering using machine learning techniques", in Proc. Passive and Active Measurement Workshop (PAM'04), Antibes Juan-les-Pins, France, April 2004.
8. S. Zander, T. Nguyen, and G. Armitage, "Automated traffic classification and application identification using machine learning", in IEEE 30th Conference on Local Computer Networks (LCN'05), Sydney, Australia, November 2005.
9. J. Erman, A. Mahanti, and M. Arlitt, "Internet traffic identification using machine learning techniques", in Proc. of 49th IEEE Global Telecommunications Conference (GLOBECOM'06), December 2006.
10. J. Erman, M. Arlitt, and A. Mahanti, "Traffic classification using clustering algorithms", in MineNet'06: Proc. 2006 SIGCOMM workshop, pp. 281-286.
11. L. Bernaille, R. Teixeira, I. Akodkenou, A. Soule, and K. Salamatian, "Traffic classification on the fly", SIGCOMM Comput. Commun. Rev., vol. 36-2, 2006.
12. J. Erman, A. Mahanti, M. Arlitt, I. Cohen, and C. Williamson, "Semisupervised network traffic classification", SIGMETRICS Performance Evaluation Review, vol. 35, no. 1, pp. 369-370, 2007.
13. T. T. Nguyen and G. Armitage, "A survey of techniques for internet traffic classification using machine learning", IEEE Commun. Surveys Tuts., vol. 10, no. 4, pp. 56-76, FOURTH QUARTER 2008.
14. M. Canini, W. Li, M. Zadnik, and A. Moore, "Experience with highspeed automated application-identification for network-management", in Proceedings of the 5th ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS'09), 209-218.
15. M. Pietrzyk, J. Costeux, G. Urvoy-Keller, and T. En-Najjary, "Challenging statistical classification for operational usage: the ADSL case", in Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference (IMC'09), pp. 122-135.
16. J. Ma, K. Levchenko, C. Kreibich, S. Savage, and G. Voelker, "Unexpected means of protocol inference", in IMC'06: Proc. 6th ACM SIGCOMM on Internet measurement. Rio, Brazil: October 2006, pp. 313-326.
17. K. Wagstaff, C. Cardie, S. Rogers and S. Schroedl, "Constrained K-means Clustering with Background Knowledge", in Proceedings of 18th international conference on Machine Learning (ICML'01), Williamstown, MA, June-July 2001, pp. 577-584.
18. M. Bilenko, S. Basu, and R. Mooney, "Integrating constraints and metric learning in semi-supervised clustering", in Proceedings of the twentyfirst international conference on Machine learning (ICML'04). ACM, New York, NY, USA, pp. 81-88.
19. D. Pelleg and D. Baras, "K-Means with Large and Noisy Constraint Sets", in Proceedings European conference on Machine Learning, 2007, pp. 674-682.
20. MAWI Working Group Traffic Archive. http://mawi.wide.ad.jp/mawi/
21. R. Pang, M. Allman, M. Bennett, J. Lee, V. Paxson, B. Tierney, "A First Look at Modern Enterprise Traffic", in Proceedings of the 5th ACM SIGCOMM/USENIX Internet Measurement Conference, October 2005.
22. Network Traffic Tracing at SIGCOMM 2008. http://www.cs.umd.edu/projects/ wifidelity/tracing/
23. K. Wagstaff, S. Basu, and I. Davidson, "When is constrained clustering beneficial, and why? " In Proceedings. of AAAAI'06, 2006.