1. Karagiannis T, Papagiannaki K, Faloutsos M. BLINC: multilevel traffic classification in the dark. Proceedings of the 2005 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM’05), Aug 22-26, 2005, Philadelphia, PA, USA. New York, NY, USA: ACM, 2005: 229-240
2. McGregor A, Hall M, Lorier P, et al. Flow clustering using machine learning techniques. Proceedings of the the 5th Passive and Active Measurement Workshop (PAM’ 04), Apri 19-20, 2004, Antibes, France. New York, NY, USA: ACM, 2004: 205-214
3. Paxson V. Empirically derived analytic models of wide-area TCP connections. IEEE/ACM Transactions on Networking, 1994, 2(4): 316-336
4. Paxson V, Floyd S. Wide-area traffic: the failure of Poisson modeling. IEEE/ACM Transactions on Networking, 1995, 3(3): 226-244
5. Roughan M, Sen S, Spatscheck O, et al. Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification. Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement Conference (IMC’04), Oct 25-27, 2004, Taormina, Italy. New York, NY, USA: ACM, 2004: 135-148
6. Bernaille L, Teixeira R, Salamatian K. Early application identification. Proceedings of the 2nd Conference on Future Networking Technologies (CoNEXT’06), Dec 4-7, 2006, Lisboa, Portugal. New York, NY, USA: ACM, 2006: 436-445
7. Crotti M, Gringoli F, Pelosato P, et al. A statistical approach to IP-level classification of network traffic. Proceedings of the IEEE International Conference on Communications (ICC’06): Vol 1, Jun 11-15, 2006, Istanbul, Turkey. Piscataway, NJ, USA: IEEE, 2006: 170-176
8. Moore A W, Zuev D. Internet traffic classification using Bayesian analysis techniques. Proceedings of ACM International Conference on Measurement & Modeling of Computer Systems (SIGMETRICS’05), Jun 6-10, 2005, Banff, Canada, New York, NY, USA: ACM, 2005: 50-60
9. Nilsson N J. Artificial intelligence: a new synthesis. San Fransisco, CA, USA: Morgan Kaufmann Publishers, 1998: 197-223
10. Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Machine Learning, 1997, 29(2/3): 131-163
11. Hall M A. Correlation-based feature selection for machine learning. Ph. D. Dissertations. Hamilton, NZ, USA: Department of Computer Science, Waikato University, 1998
12. Moore A W, Papagiannaki K. Toward the accurate identification of network applications. Proceedings of the the 6th Passive and Active Measurement Workshop (PAM’ 05), Mar 31-Apr 1, 2005, Boston, MA, USA. New York, NY, USA: ACM, 2005: 41-54 |