Traffic modeling and classification using packet train length and packet train size
作者： Dinil Mon Divakaran , Hema A. Murthy , Timothy A. Gonsalves
DOI: 10.1007/11908852_1
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摘要: Traffic modeling and classification finds importance in many areas such as bandwidth management, traffic analysis, prediction, network planning, Quality of Service provisioning anomalous detection. Network exhibits some statistically invariant properties. Earlier works show that it is possible to identify based on its statistical characteristics. In this paper, an attempt made the properties different classes using multiple parameters, namely packet train length size. Models generated these parameters are found be highly accurate classifying classes. The also useful revealing services within
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Anthony McGregor, Mark Hall, Perry Lorier, James Brunskill, Flow Clustering Using Machine Learning Techniques passive and active network measurement. ,vol. 3015, pp. 205- 214 ,(2004) , 10.1007/978-3-540-24668-8_21
Andrew W. Moore, Konstantina Papagiannaki, Toward the Accurate Identification of Network Applications Lecture Notes in Computer Science. pp. 41- 54 ,(2005) , 10.1007/978-3-540-31966-5_4
T. A. Gonsalves, Hema A. Murthy, M. A. Saifulla, Identifying patterns in internet traffic ICCC '02 Proceedings of the 15th international conference on Computer communication. pp. 859- 865 ,(2002)
W. Richard Stevens, TCP/IP Illustrated, Volume 1 : The Protocols ,(1994)
Andrew W. Moore, Denis Zuev, Internet traffic classification using bayesian analysis techniques measurement and modeling of computer systems. ,vol. 33, pp. 50- 60 ,(2005) , 10.1145/1064212.1064220
Yen-Wen Chen, Traffic behavior analysis and modeling of sub-networks International Journal of Network Management. ,vol. 12, pp. 323- 330 ,(2002) , 10.1002/NEM.451
L. Rabiner, The acoustics, speech, and signal processing society - A historical perspective IEEE ASSP Magazine. ,vol. 1, pp. 4- 10 ,(1984) , 10.1109/MASSP.1984.1162225
S.J. Roberts, D. Husmeier, I. Rezek, W. Penny, Bayesian approaches to Gaussian mixture modeling IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 20, pp. 1133- 1142 ,(1998) , 10.1109/34.730550
V. Paxson, Empirically derived analytic models of wide-area TCP connections IEEE ACM Transactions on Networking. ,vol. 2, pp. 316- 336 ,(1994) , 10.1109/90.330413
Graham Cormode, Flip Korn, S. Muthukrishnan, Divesh Srivastava, Diamond in the rough: finding Hierarchical Heavy Hitters in multi-dimensional data international conference on management of data. pp. 155- 166 ,(2004) , 10.1145/1007568.1007588