Efficient Semi-supervised Learning BitTorrent Traffic Detection with Deep Packet and Deep Flow Inspections

  • Raymond Siulai Wong
  • Teng-Sheng Moh
  • Melody Moh
Conference paper

DOI: 10.1007/978-3-642-29166-1_14

Volume 285 of the book series Communications in Computer and Information Science (CCIS)
Cite this paper as:
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

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.

Keywords

Deep Flow Inspection Deep Packet Inspection Classification BitTorrent Traffic Detection Peer to Peer Traffic 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Raymond Siulai Wong
    • 1
  • Teng-Sheng Moh
    • 1
  • Melody Moh
    • 1
  1. 1.Computer Science DepartmentSan Jose State UniversitySan JoseUSA