№2, 2014

MULTI-CLASSIFICATORY MODEL FOR NETWORK TRAFFIC

Yadigar N. Imamverdiyev, Babek R. Nabiyev

While investigating articles on monitoring traffic, you can find a lot of one-rank classification methods. Most of them are based on Naive Bayesian and neural networks methods. These methods are used to obtain fast and accurate classification. This article offers increasing productivity through a two-stage classification classifier without efficiency and accuracy loss. (pp. 68-74)

Keywords: network traffic, traffic classification, Naive Bayes, feed-forward neural network
References
  • Amanda A. Surviving Security: How to Integrate People, Process, and Technology, Auerbach Publications, 2003, pp.526, http://www.isaca.org/Journal/Past-Issues/2005/ Volume-5 /Documents/jpdf0505-Surviving-Security-How-to.pdf
  • Kim H., Claffy K., Fomenkova M., Browlee N., Barman D., Faloutsos M. Comparison of Internet Traffic Classification Tools / Internet Measurement Research Group Workshop on Application Classification and Identification, 2007, pp.11.
  • Callado A., Kamienski C., Szabo G., Gero B., Kelner J., Fernandes S., Sadok D. A Survey on Internet Traffic Identification // IEEE Communications Surveys & Tutorials, 2009, vol.11, no.3, pp.37–52.
  • Wei Li, Kaysar A., Robert D., Andrew M. Approaching Real-time Network Traffic Classification, Technical Report, 2006.
  • Auld T., Andrew M., Gull S.F. Bayesian Neural Networks for Internet Traffic Classification // IEEE Transactions on Neural Networks, 2007, vol.18, no.1, pp.223–239.
  • Shane A., Richard N. Libprotoident: Traffic Classification Using Lightweight Packet Inspection, Technical Report, 2012.
  • Manuel C., Maurizio D., Francesco G., Luca S. Traffic Classification through Simple Statistical Fingerprinting // Association for Computing Machinery's Special Interest Group on Data Communications Computer Communication Review, 2007, vol.37, no.1, pp.5–16.
  • Jun Z., Chao C., Yang X., Wanlei Z., Yong X. Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions // IEEE Transactions on Information Forensics and Security, 2012, vol.8, no.1, pp.5–15.
  • Mohammad J. Skype Traffic Classification: Naive Bayes or Neural Networks, Report, University of Toronto, 2010.
  • Jamuna A, Vinodh Ewards S.E. Efficient Flow based Network Traffic Classification using Machine Learning // International Journal of Engineering Research and Applications, 2013, vol.3, no.2, 1324–1328.