CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A SVM and K-means Clustering based Fast and Efficient Intrusion Detection System

by Alka Shrivastava, Ram Ratan Ahirwal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 6
Year of Publication: 2013
Authors: Alka Shrivastava, Ram Ratan Ahirwal
10.5120/12499-8312

Alka Shrivastava, Ram Ratan Ahirwal . A SVM and K-means Clustering based Fast and Efficient Intrusion Detection System. International Journal of Computer Applications. 72, 6 ( June 2013), 25-29. DOI=10.5120/12499-8312

@article{ 10.5120/12499-8312,
author = { Alka Shrivastava, Ram Ratan Ahirwal },
title = { A SVM and K-means Clustering based Fast and Efficient Intrusion Detection System },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 6 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number6/12499-8312/ },
doi = { 10.5120/12499-8312 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:14.042251+05:30
%A Alka Shrivastava
%A Ram Ratan Ahirwal
%T A SVM and K-means Clustering based Fast and Efficient Intrusion Detection System
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 6
%P 25-29
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The intrusion or attack in the computer network is one of the most important issues creating problems for the network managers. However many countermeasures are taken for the security of the network but continuous growth of hackers requires to maintain the defending system up to data. This paper presents a K-means and support vector machine based intrusion detection system. The support vector machine is optimal partitioning based linear classifier and at least theoretically better other classifier also because only small numbers of classes required during classification SVM with one against one technique can be the best option and the K-means clustering filters the un-useful similar data points hence reduces the training time also hence provides an overall enhanced performance by reducing the training time while maintaining the accuracy. The proposed algorithm is tested using KDD99 dataset and results show the effectiveness of the algorithm. The paper also analyzed the effect of different input parameters on classification accuracy.

References
  1. Bing Wu, Jianmin Chen, Jie Wu and Mihaela Cardei "A Survey of Attacks and Countermeasures in Mobile Ad Hoc Networks", wireless/mobile network security, 2006 Springer.
  2. Abhay Kumar Rai, Rajiv Ranjan Tewari and Saurabh Kant Upadhyay "Different Types of Attacks on Integrated MANET-Internet Communication", International Journal of Computer Science and Security (IJCSS) Volume (4): Issue (3).
  3. Dr Karim KONATE and GAYE Abdourahime "Attacks Analysis in mobile ad hoc networks:Modeling and Simulation", 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, 2011 IEEE.
  4. Farah Jemili, Dr. Montaceur Zaghdoud and Pr. Mohamed Ben Ahmed "A Framework for an Adaptive Intrusion Detection System using Bayesian Network", 2007 IEEE.
  5. Jingbo Yuan , Haixiao Li, Shunli Ding and Limin Cao "Intrusion Detection Model based on Improved Support Vector Machine", Third International Symposium on Intelligent Information Technology and Security Informatics, 2010 IEEE.
  6. Z. Muda, W. Yassin, M. N. Sulaiman and N. I. Udzir "Intrusion Detection based on K-Means Clustering and OneR Classification", 2011 IEEE.
  7. http://link. springer. com/chapter/10. 1007%2F978-3-642-14400-4_50?LI=true#.
  8. Martin Schütte " Detecting Sel?sh and Malicious Nodes in MANETs", SEMINAR: SICHERHEIT IN SELBSTORGANISIERENDEN NETZEN, HPI/UNIVERSITÄT POTSDAM, SOMMERSEMESTER 2006.
  9. http://www. personal. reading. ac. uk/~sis01xh/teaching/CY2D2/Pattern3. pdf
  10. http://voyagememoirs. com/pharmine/2008/06/22/probabilistic-neural-network-pnn/
  11. S. Nascimento, B. Mirkin and F. MouraPires "A Fuzzy Clustering Model of Data and Fuzzy c-Means", Fuzzy Systems, FUZZ IEEE 2000. The Ninth IEEE International Conference on 7-10 May 2000.
  12. R Rangadurai Karthick, Vipul P. Hattiwale and Balaraman Ravindran "Adaptive Network Intrusion Detection System using a Hybrid Approach", Communication Systems and Networks (COMSNETS), 2012 Fourth International Conference on 3-7 Jan. 2012.
  13. Chih-Wei Hsu and Chih-Jen Lin "A Comparison of Methods for Multiclass Support Vector Machines", IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 2, MARCH 2002.
  14. Eddy Mayoraz and Ethem Alpaydm "Support VectorMachines",http://www. cmpe. boun. edu. tr/~ethem/files/papers/iwann99. pdf
  15. Shigeo Abe "Analysis of Multiclass Support Vector Machines",http://www. lib. kobeu. ac. jp/repository/90000226. pdf
  16. Gidudu Anthony, Hulley Gregg and Marwala Tshilidzi "Image Classification Using SVMs: One-against-One Vs One-against-All", Proccedings of the 28th Asian Conference on Remote Sensing, 2007.
  17. P Amudha, H Abdul Rauf "Performance Analysis of Data Mining Approaches in Intrusion Detection", Process Automation, Control and Computing (PACC), 2011 International Conference on 20-22 July 2011.
  18. Sungmoon Cheong, Sang Hoon Oh and Soo-Young Lee"Support Vector Machines with Binary Tree Architecture for Multi-Class Classification", Neural Information Processing – Letters and Reviews Vol. 2, No. 3, March 2004.
  19. POWERS, D. M. W. "EVALUATION: FROM PRECISION, RECALL AND F-MEASURE TO ROC, INFORMEDNESS, MARKEDNESS & CORRELATION", Journal of Machine Learning Technologies ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011, pp-37-63.
  20. Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani "A Detailed Analysis of the KDD CUP 99 Data Set", Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009).
  21. H. Günes Kayac?k, A. Nur Zincir-Heywood, Malcolm I. Heywood "Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion DetectionDatasets" https://web. cs. dal. ca/zincir/bildiri/pst05-gnm. pdf
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System (IDS) KDD99 dataset Support Vector Machine K-means clustering

Powered by PhDFocusTM