Abstract
The peer-to-peer (P2P) technology has been well developed over the Internet. BitTorrent (BT) is one of the most popular P2P sharing protocols; BT network traffic detection has become increasingly important and yet technically challenging. In this paper we propose a new detection method that is based on an intelligent combination of Deep Packet Inspection (DPI) and Deep Flow Inspection (DFI) with semi-supervised learning. Comparing with existing methods, the new method has achieved equally high accuracy with shorter classification time. We believe that this highly effective BT detection method is not only significant to the BT community, but is also very useful to other groups that need to efficiently and correctly detect single applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chen, H., Hu, Z., Ye, Z., Liu, W.: A New Model for P2P Traffic Identification Based on DPI and DFI. In: Int. Conf. on Inf. Eng. and Computer Science, ICIECS 2009, pp. 1–3 (2009)
Erman, J., Mahanti, A., Arlitt, M., Cohen, I., Williamson, C.: Offline/Realtime Traffic Classification Using Semi-Supervised Learning. IFIP Performance (October 2007)
Klemm, A., Lindemann, C., Vernon, M.K., Waldhorst, O.P.: Characterizing the query behavior in peer-to-peer file sharing systems. In: IMC 2004: Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, pp. 55–67. ACM Press (2004)
Le, T., But, J.: Bittorrent traffic classification, CAIA Technical report 091022A, October 22 (2009), http://caia.swin.edu.au/reports/091022A/CAIA-TR-091022A.pdf
Liu, B., Li, Z., Li, Z.: Measurements of BitTorrent System Based on Netfilter. In: Int. Conf. on Computational Intelligence and Security, pp. 1470–1474 (2006)
Liu, F., Li, Z., Yu, J.: Applications Identification Based on the Statistics Analysis of Packet Length. In: Int. Symp. Information Engineering and Electronic Commerce, IEEC 2009, pp. 160–163 (2009)
Wang, C., Li, T., Chen, H.: P2P Traffic Identification Based on Double Layer Characteristics. In: Int. Conf. Information Technology and Computer Science, ITCS 2009, pp. 593–596 (2009)
Chapelle, O., Scholkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison Wesley, USA (2006)
Wong, R.S., Moh, T.-S., Moh, M.: Efficient Semi-supervised Learning BitTorrent Traffic Detection - An Extended Summary. In: Bononi, L., Datta, A.K., Devismes, S., Misra, A. (eds.) ICDCN 2012. LNCS, vol. 7129, pp. 540–543. Springer, Heidelberg (2012)
Karagiannis, T., Papagiannaki, K., Faloutsos, M.: BLINC: Multi-Level Traffic Classification in the Dark. In: Proc. ACM SIGCOMM 2005, Philadelphia, PA (August 2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wong, R.S., Moh, TS., Moh, M. (2012). Efficient Semi-supervised Learning BitTorrent Traffic Detection with Deep Packet and Deep Flow Inspections. In: Dua, S., Gangopadhyay, A., Thulasiraman, P., Straccia, U., Shepherd, M., Stein, B. (eds) Information Systems, Technology and Management. ICISTM 2012. Communications in Computer and Information Science, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29166-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-29166-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29165-4
Online ISBN: 978-3-642-29166-1
eBook Packages: Computer ScienceComputer Science (R0)