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An Approach for Intrusion Detection of IPv6 Network Based on LS-SVM Algorithm

Received: 8 July 2015     Accepted: 14 July 2015     Published: 29 July 2015
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Abstract

IPv6 has enough IP addresses to solve the problem of lack of IP address space. However, there are many security problems to be concerned. The detection ability of current intrusion detection system is poor when given less priori knowledge. In this paper, we analyze the Least Squares Support Vector Machine (LS-SVM) algorithm and the working process of snort intrusion detection system. And then we study the methods of intrusion detection in IPv6, and use LS-SVM to optimize snort intrusion detection system. Simulation results show that intrusion detection system with LS-SVM has a robust performance and has high detection efficiency

Published in Pure and Applied Mathematics Journal (Volume 4, Issue 5-1)

This article belongs to the Special Issue Mathematical Aspects of Engineering Disciplines

DOI 10.11648/j.pamj.s.2015040501.16
Page(s) 28-32
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Intrusion Detection, Least Squares Support Vector Machine, IPv6, Snort

References
[1] Qing SH, “research on intrusion detection techniques: a survey”, JOURNAL OF CHINA INSTITUTE OF COMMUNICATIONS, 25(2004), 19-29.
[2] KUMAR S, Classification and Detection of Computer Intrusions, Dissertation, Purdue University, 1995.
[3] Gomathy, A., and B. Lakshmipathi. “Network intrusion detection using Genetic algorithm and Neural Network” Advances in Computing and Information Technology, Springer Berlin Heidelberg, (2011), 399-408.
[4] The open source network intrusion detection system [EB/OL], http://www.snort.org/.
[5] Suykens J A K, Vandewalle J, “Least Squares Support Vector Machine Classifiers”, Neural Processing Letters, 9(3)( 1999),293-300.
[6] P.H. Chen, R.E. Fan, and C.J. Lin, A study on SMO-type decomposition methods or support vector machines. IEEE Transactions on Neural Networks,(2006).
[7] J.A.K.Suykens, T.Van Gestel, J.De Brabanter, B.De Moor, J.Vandewalle, “Least Squares Support Vector Machines”.Singapore: World Scientific publishing,(2002).
[8] Suykens J A K, Vandewalle, De Moor B, Optimal Control by Least Squares Support Vector Machines. Neural Networks,14(1)( 2001),23-35.
[9] Wang, Haifeng, and Dejin Hu, “Comparison of SVM and LS-SVM for regression”, Neural Networks and Brain (2005).
[10] Friedman J H., “Another Approach to Polychotomous Classification”, Technical Report. Standford University. Depart-ment of Statistics,10(1998),1895-1924.
[11] Deering S,Hinden R, Internet Protocol Version 6 (IPv6) Specification, IETF,12(1995).
[12] Andrew R. Baker,Joel Esler, “Snort Intrusion Detection and Prevention Toolkit” ,Syngress Publishing, Inc.,(2007).
[13] Martin Roesch,Chris Green,Sourcefire. SNORT Users Manual 2.9.4,11(2012).
[14] Erana, E. I. and Scheffer, T. “IPv6 Intrusion De-tection mit Snort”, In Forschungsbericht der Beuth Hochschule fur Technik Berlin, Beuth Verlag GmbH Berlin-Wien-Zurich (2010).
[15] Hogg, S. and Vyncke, E., IPv6 Security. Cisco Press, Indianapolis, IN 46240 USA.,(2009).
[16] Loshin, Pete. “IPv6: Theory, Protocol, and Practice”. San Fransisco: Morgan Kaufmann Publishers, (2003).
[17] Kent S.IP encapsulating security payload (ESP), RFC4203 [EB/OL]. http://www.ietf. org/rfc/rfc4203.txt,(2005).
[18] K. Pelckmans, J.A.K. Suykens, T. Van Gestel, et.al, Vandewalle, LS-SVMlab Toolbox User’s Guide. http://www.esat.kuleuven.ac.be/sista/LS-SVMlab.
[19] KDD Cup 99 DATA.http://kdd.ics.uci.edu.
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  • APA Style

    Liu Jing. (2015). An Approach for Intrusion Detection of IPv6 Network Based on LS-SVM Algorithm. Pure and Applied Mathematics Journal, 4(5-1), 28-32. https://doi.org/10.11648/j.pamj.s.2015040501.16

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    ACS Style

    Liu Jing. An Approach for Intrusion Detection of IPv6 Network Based on LS-SVM Algorithm. Pure Appl. Math. J. 2015, 4(5-1), 28-32. doi: 10.11648/j.pamj.s.2015040501.16

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    AMA Style

    Liu Jing. An Approach for Intrusion Detection of IPv6 Network Based on LS-SVM Algorithm. Pure Appl Math J. 2015;4(5-1):28-32. doi: 10.11648/j.pamj.s.2015040501.16

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  • @article{10.11648/j.pamj.s.2015040501.16,
      author = {Liu Jing},
      title = {An Approach for Intrusion Detection of IPv6 Network Based on LS-SVM Algorithm},
      journal = {Pure and Applied Mathematics Journal},
      volume = {4},
      number = {5-1},
      pages = {28-32},
      doi = {10.11648/j.pamj.s.2015040501.16},
      url = {https://doi.org/10.11648/j.pamj.s.2015040501.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pamj.s.2015040501.16},
      abstract = {IPv6 has enough IP addresses to solve the problem of lack of IP address space. However, there are many security problems to be concerned. The detection ability of current intrusion detection system is poor when given less priori knowledge. In this paper, we analyze the Least Squares Support Vector Machine (LS-SVM) algorithm and the working process of snort intrusion detection system. And then we study the methods of intrusion detection in IPv6, and use LS-SVM to optimize snort intrusion detection system. Simulation results show that intrusion detection system with LS-SVM has a robust performance and has high detection efficiency},
     year = {2015}
    }
    

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    AB  - IPv6 has enough IP addresses to solve the problem of lack of IP address space. However, there are many security problems to be concerned. The detection ability of current intrusion detection system is poor when given less priori knowledge. In this paper, we analyze the Least Squares Support Vector Machine (LS-SVM) algorithm and the working process of snort intrusion detection system. And then we study the methods of intrusion detection in IPv6, and use LS-SVM to optimize snort intrusion detection system. Simulation results show that intrusion detection system with LS-SVM has a robust performance and has high detection efficiency
    VL  - 4
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Author Information
  • College of Mathematics and Information Science, Weinan Normal University, Weinan, P. R. China

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