[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Steady-State Topology Discovery of Target Networks Based on Statistics Method

  • Conference paper
  • First Online:
Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11635))

Included in the following conference series:

  • 2229 Accesses

Abstract

The progress of target network topology discovery research is slow due to the uncertainty of the target network range, and large-scale network measurement activities lead to a large accumulation of “false links” in the results, it is difficult to obtain a relatively accurate network topology. In this paper, we proposed a statistical-based target network steady-state topology discovery method. By statistical analyzing the characteristics of the measured data, we have proposed corresponding solutions to network boundary recognition, “false links” deletion and network steady-state topology construction, so that a relatively complete and accurate target network steady topology can be obtained. We also use this method to probe the HK (Hong Kong) and TW (Tai Wan) network and compare it with the data of CAIDA in the same period. Not only do the number of nodes and edges found are increased by two or three orders of magnitude, but also the number of “false links” in the results is greatly reduced.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • 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

Similar content being viewed by others

References

  1. Motamedi, R., Rejaie, R., Willinger, W.: A survey of techniques for internet topology discovery. IEEE Commun. Surv. Tutorials 17, 1044–1065 (2014)

    Google Scholar 

  2. Floyd, S., Paxson, V.: Difficulties in simulating the Internet (2001)

    Google Scholar 

  3. Claffy, K., Luckie, M., Dhamdhere, A., et al.: Bdrmap: inference of borders between IP networks. In: ACM on Internet Measurement Conference, pp. 381–396 (2016)

    Google Scholar 

  4. Donnet, B., Friedman, T.: Internet topology discovery: a survey (2007)

    Google Scholar 

  5. Shavitt, Y., Shir, E.: DIMES: let the internet measure itself. ACM SIGCOMM Comput. Commun. Rev. 35(5), 71–74 (2005)

    Google Scholar 

  6. Beverly, R., Berger, A., Xie, G.G.: Primitives for active internet topology mapping: toward high-frequency characterization. In: ACM SIGCOMM Conference on Internet Measurement, pp. 165–171 (2010)

    Google Scholar 

  7. “Traceroute”: ftp://ftp.ee.lbl.gov/traceroute.tar.gz

  8. Toren, M.C.: Tcptraceroute: an implementation of traceroute using TCP SYN packets. Man page (2001). http://michael.toren.net/code/tcptraceroute/

  9. Surhone, L.M., Tennoe, M.T., Henssonow, S.F., et al.: TCP/IP Model (2010)

    Google Scholar 

  10. Yang, W., Dong, P., et al.: A MPTCP scheduler for web transfer. CMC: Comput. Mater. Continua 57(2), 205–222 (2018)

    Google Scholar 

  11. Wang, J., Ju, C., et al.: A PSO based energy efficient coverage control algorithm for wireless sensor networks. CMC: Comput. Mater. Continua 56(3), 433–446 (2018)

    Google Scholar 

  12. Gharaibeh, M., Shah, A., Huffaker, B., et al.: A look at router geolocation in public and commercial databases. In: Internet Measurement Conference, pp 463–469 (2017)

    Google Scholar 

  13. Komosný, D., Vozňák, M., Rehman, S.U., et al.: Location accuracy of commercial IP address geolocation databases. Inf. Technol. Control 46(3), 333–344 (2017)

    Google Scholar 

  14. Wang, G., et al.: Topology discovery of backbone network based on multi-message combination. Comput. Eng. Sci. 29(8), 4–6 (2007)

    Google Scholar 

  15. Liu, S., Liu, F., Zhao, F., et al.: IP city-level geolocation based on the PoP-level network topology analysis. In: International Conference on Information Communication and Management, pp 109–114. IEEE (2016)

    Google Scholar 

Download references

Acknowledgment

This work was supported by the National Key R&D Program of China (No. 2016YFB0801303, 2016QY01W0105), the National Natural Science Foundation of China (No.61309007, U1636219, 61602508, 61772549, U1736214, 61572052) and Plan for Scientific Innovation Talent of Henan Province (No. 2018JR0018).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yan Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, D., Liu, Y., Chen, J. (2019). Steady-State Topology Discovery of Target Networks Based on Statistics Method. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11635. Springer, Cham. https://doi.org/10.1007/978-3-030-24268-8_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24268-8_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24267-1

  • Online ISBN: 978-3-030-24268-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics