CFP last date
20 March 2024
Reseach Article

Kernelized Extreme Learning Machine with Levenberg-Marquardt Learning Approach towards Intrusion Detection

by V. Jaiganesh, P. Sumathi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 54 - Number 14
Year of Publication: 2012
Authors: V. Jaiganesh, P. Sumathi

V. Jaiganesh, P. Sumathi . Kernelized Extreme Learning Machine with Levenberg-Marquardt Learning Approach towards Intrusion Detection. International Journal of Computer Applications. 54, 14 ( September 2012), 38-44. DOI=10.5120/8638-2577

@article{ 10.5120/8638-2577,
author = { V. Jaiganesh, P. Sumathi },
title = { Kernelized Extreme Learning Machine with Levenberg-Marquardt Learning Approach towards Intrusion Detection },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 54 },
number = { 14 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { },
doi = { 10.5120/8638-2577 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:55:43.119909+05:30
%A V. Jaiganesh
%A P. Sumathi
%T Kernelized Extreme Learning Machine with Levenberg-Marquardt Learning Approach towards Intrusion Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 54
%N 14
%P 38-44
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

Network and system security is of vital importance in the present data communication environment. Hackers and intruders can create many successful attempts to cause the crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to prevent these threats are emerging together with the secured system evolution. Intrusion Detection Systems (IDS) are one of these solutions. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviours of users, and then these behaviours will be considered an attack or a normal behaviour. There are several techniques which exist at present to provide more security to the network, but most of these techniques are static. On the other hand, intrusion detection is a dynamic one, which can give dynamic protection to the network security by observing the attack. In recent times, Extreme Learning Machine (ELM) has been extensively applied to provide potential solutions for the IDS problem. But, the practicability of ELM is affected because of the complexity in choosing the suitable ELM parameters. Hence, in this paper sigma (?) of the radial basis kernel function is tuned using Levenberg-Marquardt (LM) learning and proposed kernelized Extreme Learning Machine with LM. In order to obtain a converged solution, LM learning is utilized. The experiment is carried out with the help of WEKA by using KDD Cup 1999 dataset and the results indicate that the proposed technique can achieve higher detection rate, very low false alarm rate and to achieve high accuracy than the regular ELM algorithms. This method is used to decrease the space densisty of the data.

  1. S. Mukkamala, G. Janoski and A. Sung, "Intrusion detection using neural networks and support vector machines", Proceedings of International Joint Conference on Neural Networks (IJCNN '02), Vol. 2, Pp. 1702–1707, 2002.
  2. Snehal A. Mulay, P. R. Devale and G. V. Garje, "Intrusion Detection System using Support Vector Machine and Decision Tree", International Journal of Computer Applications, Vol. 3, No. 3, Pp. 40-43, 2010.
  3. D. Anderson, T. Frivold and A. Valdes, "Next-generation intrusion detection expert system (NIDES): a summary", Technical Report SRI-CSL-95-07. Computer Science Laboratory, SRI International, Menlo Park, CA, 1995.
  4. S. Axelsson, "Research in intrusion detection systems: a survey", Technical Report TR 98-17 (revised in 1999). Chalmers University of Technology, Goteborg, Sweden, 1999.
  5. S. Freeman, A. Bivens, J. Branch and B. Szymanski, "Host-based intrusion detection using user signatures", Proceedings of the Research Conference. RPI, Troy, NY, 2002.
  6. K. Ilgun, R. A. Kemmerer and P. A. Porras, "State transition analysis: A rule-based intrusion detection approach", IEEE Trans. Software Eng, Vol. 21, No. 3, Pp. 181–199, 1995.
  7. D. Marchette, "A statistical method for profiling network traffic", Proceedings of the First USENIX Workshop on Intrusion Detection and Network Monitoring, Santa Clara, CA, Pp. 119–128, 1999.
  8. R. G. Bace, "Intrusion Detection", Macmillan Technical Publishing, 2000.
  9. B. V. Dasarathy, "Intrusion detection, Information Fusion", Vol. 4, No. 4, Pp. 243-245, 2003.
  10. M. Cramer, James Cannady and Jay Harrell, "New Methods of Intrusion Detection using Control-Loop Measurement", Proceedings of the Technology in Information Security Conference, Pp 1-10, 1995.
  11. Zhang Hongmei, "ELM ensemble intrusion detection model based on Rough Set feature reduct", Chinese Control and Decision Conference (CCDC '09), Pp. 5604–5608, 2009.
  12. Defeng Wang, D. S. Yeung and E. C. Tsang, "Weighted Mahalanobis Distance Kernels for Support Vector Machines", IEEE Transactions on Neural Networks, Vol. 18, No. 5, Pp. 1453-1462, 2007.
  13. J. F. C. Joseph, Bu-Sung Lee, A. Das and Boon-Chong Seet, "Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using ELM and FDA", IEEE Transactions on Dependable and Secure Computing, Vol. 8, No. 2, Pp. 233-245, 2011.
  14. Wun-Hwa Chen, Sheng-Hsun Hsu and Hwang-Pin Shen, "Application of ELM and ANN for intrusion detection", Computers & Operations Research, Vol. 32, Pp. 2617-2634, 2003.
  15. G. -B. Huang, Q. -Y. Zhu, C. -K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 25–29 July 2004.
  16. Witten, I. H. , and Frank E. (1999) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, San Francisco.
  17. KDD Cup network intrusion dataset, http://kdd. ics. uci. edu/databases/kddcup99/kddcup99. html
  18. Kyaw Thet Khaing, "Enhanced Features Ranking and Selection using Recursive Feature Elimination (RFE) and k-Nearest Neighbor Algorithms in Support Vector Machine for Intrusion Detection System", International Journal of Network and Mobile Technologies, Vol. 1, No. 1, Pp. 8-14, 2010.
  19. Liang N-Y, Huang G. B, Saratchandran P, Sundararjan N (2006) A fast and accurate on-line sequential learning algorithm for feed forward networks. IEEE Trans Neural Netw 17(6):1411-1423.
  20. A. R. Barron, Universal approximation bounds for superposition of sigmoidal function, IEEE Trans. Inf Theory 39(3) (1993) 930-945.
  21. P. L Bartlett,"The Sample complexity of pattern classification with Neural Networks:The size of the Weight is More Important than the Size of the Network," IEEE Trans. Information Theory,Vol. 44,no. 2,pp. 525 536,Mar. 1998.
  22. G. B Huang, Q. Y. Zhu, and C. K. Siew,"Extreme Learning Machine: Theory and Application," Neurocomputing,Vol. 70,pp, 489-501, 2006.
Index Terms

Computer Science
Information Sciences


Intrusion Detection System (IDS) Extreme Learning Machine (ELM) Radial Basis Function (RBF) Kernel Function Leave-One-Out (LOO) Kernel Partial Least Squares (K-PLS)