DSTC - Related Papers and Interim Reports

By members of CAIA

Journal and Conference Papers

• Thuy T. T. Nguyen, Grenville Armitage, Philip Branch and Sebastian Zander, "Timely and Continuous Machine-Learning-Based Classification for Interactive IP Traffic", IEEE/ACM Transactions on Networking, vol. 20 no. 6 pp. 1880-1894, December 2012. bibtex
• Sebastian Zander, Thuy Nguyen and Grenville Armitage, "Sub-flow packet sampling for scalable ML classification of interactive traffic", in The 37th IEEE Conference on Local Computer Networks (LCN12), pp. 68-75. Clearwater, Florida, October 2012. bibtex
• T.T.T. Nguyen, G. Armitage, "A Survey of Techniques for Internet Traffic Classification using Machine Learning", IEEE Communications Surveys & Tutorials, vol. 10 no. 4 pp. 56-76, 2008.
• T.T.T. Nguyen, G. Armitage, "Clustering to Assist Supervised Machine Learning for Real-Time IP Traffic Classification", IEEE International Conference on Communications (ICC 2008), pp. 5857-5862, Beijing, China, 19-23 May 2008. 
• T.T.T. Nguyen, G. Armitage, "Synthetic Sub-flow Pairs for Timely and Stable IP Traffic Identification", Proc. Australian Telecommunication Networks and Application Conference 2006, Melbourne, Australia 4-6 December 2006.
• T.T.T.Nguyen, G.Armitage, "Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networks", Proc. IEEE 31st Conference on Local Computer Networks, Tampa, Florida, USA, November 2006.
• N. Williams, S. Zander, G. Armitage, "A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification", SIGCOMM Computer Communication Review, October 2006.
• S. Zander, N. Williams, G. Armitage, "Internet Archeology: Estimating Individual Application Trends in Incomplete Historic Traffic Traces", (poster) Passive and Active Measurement Workshop (PAM 2006),  Adelaide, Australia, March 30 - 31, 2006. [full paper]
• S. Zander, T.T.T. Nguyen, G. Armitage, "Automated Traffic Classification and Application Identification using Machine Learning", Proceedings of IEEE 30th Conference on Local Computer Networks (LCN 2005), Sydney, Australia, 15-17 November 2005.
• S. Zander, T.T.T. Nguyen, G. Armitage, "Self-learning IP Traffic Classification based on Statistical Flow Characteristics", (short paper) Passive and Active Measurement Workshop (PAM 2005), Boston, USA, March / April 2005.

Tech Reports and Talks

• S. Zander, N. Williams, G. Armitage, "Internet Archeology: Estimating Individual Application Trends in Incomplete Historic Traffic Traces", (pdf) CAIA Technical Report 060313A, March 2006.
• N. Williams, S. Zander, G. Armitage, "Evaluating Machine Learning Algorithms for Automated Network Application Identification", (pdf) CAIA Technical Report 060410B, April 2006.
• N. Williams, S. Zander, G. Armitage, "Evaluating Machine Learning Methods for Online Game Traffic Identification", (pdf) CAIA Technical Report 060410C, April 2006.
• S. Zander, "Misclassification of Game Traffic based on Port Numbers: A Case Study using Enemy Territory", (pdf) CAIA Technical Report 060410D, April 2006.
• N. Williams, "netAI - Network Traffic based Application Identifier", (pdf) CAIA Technical Report 060410E, April 2006.

By the wider community

Machine Learning for Traffic Classification

• L. Bernaille, R. Teuxeira, I. Akodkenou, A. Soule, K. Salamatian, “Traffic Classification on the Fly”, ACM SIGCOMM Computer Communication Review, vol. 36, no. 2, April 2006.
• T. Karagiannis, K. Papagiannaki, and M. Faloutsos, “BLINC: Multilevel Traffic Classification in the Dark”, ACM Sigcomm 2005, Philadelphia, PA, USA, August 2005.
• Denis Zuev, Andrew Moore, "Internet traffic classification using bayesian analysis techniques", ACM SIGMETRICS 2005, Banff, Canada, June, 2005.
• Denis Zuev, Andrew Moore, “Traffic Classification using a Statistical Approach”, Passive & Active Measurement Workshop, Boston, U.S.A, April 2005.
• M. Roughan, S. Sen, O. Spatscheck, N. Duffield, “Class-of-Service Mapping for QoS: A statistical signature-based approach to IP traffic classification”, ACM SIGCOMM Internet Measurement Workshop 2004, Taormina, Sicily, Italy, 2004.
• A. McGregor, M. Hall, P. Lorier, J. Brunskill, “Flow Clustering Using Machine Learning Techniques”, Passive & Active Measurement Workshop 2004 (PAM 2004), France, April 19-20, 2004.
• James P. Early, Carla E. Brodley, and Catherine Rosenburg, "Behavioral Authentication of Server Flows", Proceedings of the 19th Annual Computer Security Applications Conference, Las Vegas, NV, USA, December 2003.
• A. Soule, K. Salamatian, N. Taft, R. Emilion, and K. Papagiannaki, “Flow Classification by Histograms or How to Go on Safari in the Internet”, In ACM Sigmetrics, New York, U.S.A., June, 2004.
• T. Dunnigan, G. Ostrouchov, “Flow Characterization for Intrusion Detection”, Oak Ridge National Laboratory, Technical Report, November 2000.

Traffic Measurement and Classification

• A. Moore, and K. Papagiannaki, “Toward the Accurate Identification of Network Applications”, to appear in Passive & Active Measurement Workshop, Boston, U.S.A., April, 2005.
• Cisco IOS Documentation, “Network-Based Application Recognition and Distributed Network-Based Application Recognition“, (as of February 2005).
• Thomas Karagiannis, Andre Broido, Nevil Brownlee, kc claffy, “Is P2P dying or just hiding?”, Proceedings of Globecom 2004, November/December 2004.
• S. Sen, O. Spatscheck, D. Wang, “Accurate, Scalable In­Network Identification of P2P Traffic Using Application Signatures”, WWW 2004, New York, USA, May 2004.
• K. Lan, J. Heidemann, “On the correlation of Internet flow characteristics”, Technical Report ISI-TR-574, USC/Information Sciences Institute, July, 2003.
• S. McCreary and k. claffy, “Trends in wide area IP traffic patterns - A view from Ames Internet Exchange'', in ITC Specialist Seminar, Monterey, CA, 18-20 Sep 2000.

Machine Learning Techniques and Algorithms

• Ian H. Witten, Eibe Frank,  "Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)", Morgan Kaufmann, June 2005.
• Tom M. Mitchell, “Machine Learning”, McGraw-Hill Education (ISE Editions), December 1997.
• Tom M. Mitchell, “Does Machine Learning Really Work?”, AI Magazine 18(3), pp. 11-20, 1997.
• T. G. Dietterich, “Machine-Learning Research – Four Current Directions”, American Association for Artificial Intelligence, 1997.