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Reseach Article

A Parallel Support Vector Machine for Network Intrusion Detection System

by Preeti Yadav, Divakar Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 13
Year of Publication: 2013
Authors: Preeti Yadav, Divakar Singh
10.5120/13170-0771

Preeti Yadav, Divakar Singh . A Parallel Support Vector Machine for Network Intrusion Detection System. International Journal of Computer Applications. 75, 13 ( August 2013), 11-14. DOI=10.5120/13170-0771

@article{ 10.5120/13170-0771,
author = { Preeti Yadav, Divakar Singh },
title = { A Parallel Support Vector Machine for Network Intrusion Detection System },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 13 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 11-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number13/13170-0771/ },
doi = { 10.5120/13170-0771 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:44:10.284730+05:30
%A Preeti Yadav
%A Divakar Singh
%T A Parallel Support Vector Machine for Network Intrusion Detection System
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 13
%P 11-14
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper proposes a parallel SVM for detecting intrusions in computer network. The success of any Intrusion Detection System (IDS) is a complex problem due to its non-linearity and quantitative or qualitative traffic stream with irrelevant and unnecessary features. How to choose effective and key features of IDS is a very important topic in information security. Since the training data set size may be very large with a large number of parameters, which makes it difficult to handle single SVM therefore parallel LMM concept is proposed in this paper for distributing data files to n different sets of n different devices that reduce computational complexity, computational power and memory for each machine. The proposed method is simple but very reliable parallel operation SVM and can be used for large data files and unbalanced method also provides the flexibility to change depending on the size of the data file, the processor and the memory available on the various units. The proposed method is simulated using MATLAB and the result shows its superiority.

References
  1. Sandya Peddabachigari, Ajith Abraham, Crina Grosan, Johanson Thomas. Modeling Intrusion Detection Systems Using Hybrid Intelligent Systems. Journal of Network and Computer Applications-2005.
  2. Jun GUO , Norikazu Takahashi, Wenxin Hu . An Efficient Algorithm for Multi-class Support Vector Machines. IEEE-2008.
  3. Latifur Khan, Mamoun Awad, Bhavani Thuraisingham. A new intrusion detection system using support vector machines and hierarchical clustering. The VLDB Journal DOI 10. 1007/s00778-006-0002 , 2007.
  4. V. N. Vapnik. The nature of statistical learning theory. Springer-Verlag,New York. NY, 1995.
  5. Xiaodan Wang, Zhaohui Shi, Chongming Wu and Wei Wang. An Improved Algorithm for Decision-Tree-Based SVM. IEEE-2006.
  6. Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to data mining. Pearson Education.
  7. K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2:265–292, 2001.
  8. YMahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani. A detailed analysis of KDD CUP'99 data set. IEEE-2009.
  9. ttp://kdd. ics. uci. edu/databases/kddcup99/kddcup99. html
  10. C. W. Hsu, C. J. Lin. A comparison of methods for multiclass support vector machines. IEEE Trans. On Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
  11. Snehal Mulay, P. R. Devale, G. V. Garje. Decision Tree based Support Vector Machine for Intrusion Detection. ICNIT-2010, unpublished.
  12. Lili Cheng, Jianpei Zhang, Jing Yang, Jun Ma. An improved Hierarchical Multi-Class Support Vector Machine with Binary Tree Architecture" 978-0-7695-3112- 0/08 2008 IEEE DOI 10. 1109/ICICSE. 2008
  13. Razieh Baradaran and Mahdieh HajiMohammadHosseini "Intrusion Detection System based on Support Vector Machine and BN-KDD Data Set", 7thSASTech 2013, Iran, Bandar-Abbas. 7-8 March, 2013.
  14. Sreeja M. S. , Aarcha Anoop 'New Genetic Algorithm Based Intrusion Detection System for SCADA", International Journal of Engineering Innovation & Research Volume 2, Issue 2, ISSN: 2277 – 5668.
  15. Megha Bandgar, Komal dhurve, Sneha Jadhav,Vicky Kayastha,Prof. T. J Parvat "Intrusion Detection System using Hidden Markov Model (HMM)", IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 10, Issue 3 (Mar. - Apr. 2013), PP 66-70.
  16. Alma Cemerlic, Li Yang, Joseph M. Kizza "Network Intrusion Detection Based on Bayesian Networks", University of Tennessee at Chattanooga Chattanooga, TN 37403.
  17. S. A. Joshi, Varsha S. Pimprale "Network Intrusion Detection System (NIDS) based on Data Mining", International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 1, January 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Parallel Support Vector Machine Binary Classification