Flow classification using clustering and association rule mining

2010 15th IEEE International Workshop on Computer Aided Modeling, Analysis and Design of Communication Links and Networks, CAMAD 2010

Volumn , Issue , 2010, Pages 76-80

  Chaudhary, Umang K ^a   Papapanagiotou, Ioannis ^a   Devetsikiotis, Michael ^a  

^a North Carolina State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APRIORI; ASSOCIATION RULE MINING; BEHAVIOR PATTERNS; CLASSIFICATION MODELS; DATA MINING TECHNIQUES; FLOW CLASSIFICATION; FLOW PARAMETERS; RULE MINING; TRAFFIC CLASSIFICATION;

ASSOCIATION RULES; CLUSTER ANALYSIS; COMPUTER AIDED ANALYSIS; COMPUTER NETWORKS; DATA MINING; OPTICAL COMMUNICATION; TELECOMMUNICATION TRAFFIC;

CLUSTERING ALGORITHMS;

EID: 79951501937     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CAMAD.2010.5686959     Document Type: Conference Paper

References (14)

1. F. Baker, B. Foster, and C. Sharp. Cisco architecture for lawful intercept in IP networks. Internet Engineering Task Force, RFC, 3924, 2004.
2. C. Borgelt and R. Kruse. Induction of association rules: Apriori implementation. In Compstat: Proceedings in Computational Statistics: 15th Symposium Held in Berlin, Germany, 2002, page 395. Physica Verlag, 2002.
3. WAND Trace Catalogue. http://www.wand.net.nz/wits/catalogue.php.
4. C. Dewes, A. Wichmann, and A. Feldmann. An analysis of Internet chat systems. In Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement, pages 51-64. ACM, 2003. (Pubitemid 40730639)
5. J. Erman, A. Mahanti, and M. Arlitt. Internet traffic identification using machine learning. In Proceedings of IEEE GlobeCom. Citeseer, 2006.
6. C. Fraley and A.E. Raftery. MCLUST version 3 for R: Normal mixture modeling and model-based clustering. Technical report, Citeseer, 2006.
7. M. Iliofotou, H. Kim, M. Faloutsos, M. Mitzenmacher, P. Pappu, and G. Varghese. Graph-based P2P traffic classification at the internet backbone. In IEEE Global Internet Symposium. Citeseer, 2009.
8. T. Karagiannis, A. Broido, and M. Faloutsos. Transport layer identification of P2P traffic. In Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, pages 121-134. ACM, 2004. (Pubitemid 40372037)
9. David Kotz, Tristan Henderson, Ilya Abyzov, and Jihwang Yeo. CRAWDAD trace dart-mouth/campus/tcpdump/fall01 (v. 2004-11-09). Downloaded from http://crawdad.cs.dartmouth.edu/dartmouth/campus/tcpdump/fall01, November 2004.
10. A.W. Moore and K. Papagiannaki. Toward the accurate identification of network applications. Passive and Active Network Measurement, pages 41-54, 2005. (Pubitemid 41252735)
11. T.T.T. Nguyen and G. Armitage. A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials, 10(4):56-76, 2008.
12. I. Papapanagiotou and M. Devetsikiotis. Aggregation Design Methodologies for Triple Play Services. In IEEE CCNC 2010, Las Vegas, USA, pages 1-5, 2010.
13. V. Paxson. Bro: A system for detecting network intruders in real-time. Comput. Networks, 31(23):2435-2463, 1999.
14. J. Xu, J. Fan, M. Ammar, and S.B. Moon. On the design and performance of prefix-preserving IP traffic trace anonymization. In Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement, page 266. ACM, 2001.