Skip to main content
SpringerLink
Log in

Machine Learning Based Autonomous Network Flow Identifying Method

  • Conference paper
Wireless Internet (WICON 2011)

Included in the following conference series:

  • 918 Accesses

Abstract

Recently, various applications and services start to be used in the Internet. Load balancing the increasing network traffic in real time can affect the network quality. The flow control technologies become much more important than before. Our research project proposes an intelligent network flow identifying method, smart flow, which is based on the learning algorithm. In this paper, we suggest to utilize the SOM for learning the properties of packets, such as timestamp, source and destination. Based on our proposed normalization, IP network flows can be formed autonomously during the learning process. Furthermore, the combination use of the new normalization with the GHSOM can classify the sub-IP flows belongs to the same flow. This paper indicates that a flow shall consist of several sub-IP flows, and sub-IP flow shall consist of several IP packets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mori, T., Takine, T., Pan, J., Kawahara, R., Uchida, M., Goto, S.: Identifying Heavy-Hitter Flows from Sampled Flow Statistics. IEICE Transaction 90-B(11), 3061–3072 (2007)

    Article  Google Scholar 

  2. sFlow, http://www.sflow.org/

  3. NetFlow, http://www.cisco.com

  4. Openflow, http://www.openflowswitch.org/

  5. Claise, B.: Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of IP Traffic Flow Information, RFC 5401 (January 2008)

    Google Scholar 

  6. Sadasivan, G., Brownlee, N., Clasise, B., Quittek, J.: Architecture for IP Flow Information Export, RFC 5470 (March 2009)

    Google Scholar 

  7. Kohonen, T.: Self-Organizing Maps. Springer (2000)

    Google Scholar 

  8. SOM_PAK, http://www.cis.hut.fi/research/som_lvq_pak.shtml

  9. IPv6 Specification, Internet Engineering Task Force, RFC 2460 (December 1998)

    Google Scholar 

  10. Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13(6), 1331–1341 (2002)

    Article  MATH  Google Scholar 

  11. Dittenbach, M., Merki, D., Rauber, A.: The Growing Hierarchical Self-Organizing Map. In: Proc. of the International Joint Conference on Neural Networks (IEEE IJCNN 2000) (July 2000)

    Google Scholar 

  12. Palomo, E.J., Domínguez, E., Luque, R.M., Muñoz, J.: A New GHSOM Model Applied to Network Security. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 680–689. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Physics, Electrical and Computer Engineering, Graduate School of Engineering, Yokohama National University, 79-1 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501, Japan

    Hongbo Shi & Tomoki Hamagami

  2. Information Technology Service Center, Yokohama National University, 79-1 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501, Japan

    Haoyuan Xu

Authors
  1. Hongbo Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomoki Hamagami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haoyuan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Xi’an Jiaotong University, Xianning West Road 28, 710049, Xi’an, Shaanxi, China

    Pinyi Ren  & Chao Zhang  & 

  2. University of California, 3013 Kemper Hall, 95616, Davis, CA, USA

    Xin Liu

  3. Polytechnic Institute of New York University, 6 Metrotech Center, 11201, New York, NY, USA

    Pei Liu

  4. University of Nebraska-Lincoln, 200B Peter Kiewit Institute, 68588, Omaha, NE, USA

    Song Ci

Rights and permissions

Reprints and permissions

Copyright information

© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Shi, H., Hamagami, T., Xu, H. (2012). Machine Learning Based Autonomous Network Flow Identifying Method. In: Ren, P., Zhang, C., Liu, X., Liu, P., Ci, S. (eds) Wireless Internet. WICON 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30493-4_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30493-4_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30492-7

  • Online ISBN: 978-3-642-30493-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation