CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

An Ensemble Classification Approach for Intrusion Detection

by Riyad. A. M, M. S Irfan Ahmed
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 2
Year of Publication: 2013
Authors: Riyad. A. M, M. S Irfan Ahmed
10.5120/13836-1402

Riyad. A. M, M. S Irfan Ahmed . An Ensemble Classification Approach for Intrusion Detection. International Journal of Computer Applications. 80, 2 ( October 2013), 37-42. DOI=10.5120/13836-1402

@article{ 10.5120/13836-1402,
author = { Riyad. A. M, M. S Irfan Ahmed },
title = { An Ensemble Classification Approach for Intrusion Detection },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 2 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 37-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number2/13836-1402/ },
doi = { 10.5120/13836-1402 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:51.526183+05:30
%A Riyad. A. M
%A M. S Irfan Ahmed
%T An Ensemble Classification Approach for Intrusion Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 2
%P 37-42
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Increased cyber attacks in various forms compel everyone to implement effective intrusion detection systems for protecting their information wealth. From last two decades, there has been extensive research going on in intrusion detection system development using various techniques. But, designing detection systems producing maximum accuracy with minimum false positive is yet a challenging task for the research community. Ensemble method is one of the major developments in the field of machine learning. In this research work, new ensemble classification method is proposed from different classifiers. Support vector machine techniques, artificial neural network and random forest are used for classification. Ensemble model is formed for producing better result. The model shows promising result for all classes of attacks.

References
  1. Gogoi P, Borah B, Bhattacharyya D. "Anomaly detection analysis of intrusion data using supervised & unsupervised approach. " Journal of Convergence Information Technology 2010.
  2. J. P. Anderson. "Computer security threat monitoring and surveillance". Technical report, James P. Anderson Company, Fort Washington, Pennsylvania, April 1980.
  3. Dorothy E. Denning. An intrusion-detection model. IEEE Trans. Software Eng. , 1987.
  4. Stephen E. Smaha, "Haystack: An intrusion detection system. " In Proceedings of the Fourth Aerospace Computer Security Applications Conference, December 1988.
  5. Lars Kai Hansen and Peter Salamon. "Neural Network Ensembles. " IEEE Transactions on Pattern Analysis and Machine Intelligence, October 1990.
  6. H. Debar, M. Becker and D. Siboni, "A Neural Network Component for an IntrusionDetection System", In Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, May 1992.
  7. S. Mukkamala, A. H. Sung and A. Abraham, "Modeling Intrusion Detection Systems Using Linear Genetic Programming Approach. " LNCS 3029, Springer Hiedelberg, 2004.
  8. S. Chebrolu, , A. Abraham, and J. P. Thomas, "Feature Deduction and Ensemble Design of Intrusion Detection Systems. " International Journal of Computers and Security, Vol 24, Issue 4, June 2005.
  9. Ajith Abraham and Crina Grosan. "Evolving Intrusion Detection Systems", volume 13 of Studies in Computational Intelligence, Springer-Verlag, Berlin, Heidelberg, 2006.
  10. Ajith Abraham, Crina Grosan, and Carlos Martin-vide. "Evolutionary Design of Intrusion Detection Programs. " International Journal of Network Security, November 2006.
  11. Anazida Zainal, Mohd Aizaini Maarof, Siti Mariyam Shamsuddin, and Ajith Abraham. "Ensemble of One-Class Classi?ers for Network Intrusion Detection System. " In Proccedings of the 4th International Conference on Information Assurance and Security (IAS), IAS '08, IEEE Computer Society. Napoli, Italy, September 2008.
  12. Emna Bahri, Nouria Harbi, and Hoa Nguyen Huu. "Approach Based Ensemble Methods for Better and Faster Intrusion Detection. " In Proceedings of the 4th International Conference on Computational Intelligence in Security for Information Systems, Lecture Notes in Computer Science, Torremolinos-Malaga, Spain, June 2011. Springer.
  13. Silvia Gonz´alez, Javier Sedano, Alvaro Herrero, Bruno Baruque, and Emilio Corchado. "Testing ensembles for intrusion detection: On the identi?cation of mutated network scans. " In Proceedings of the 4th international conference on Computational intelligence in security for information systems, CISIS'11, Torremolinos-Malaga, Spain, June 2011. Springer-Verlag.
  14. Peng Zhang, Xingquan Zhu, Yong Shi, Li Guo, and Xindong Wu. "Robust ensemble learning for mining noisy data streams. " Decision Support Systems, January 2011.
  15. Gianluigi Folino, Clara Pizzuti, and Giandomenico Spezzano. "GP Ensemble for Distributed Intrusion Detection Systems. " In Proceedings of the 3rd International Conference on Advances in Pattern Recognition (ICAPR), Bath, UK, August 2005.
  16. Gianluigi Folino, Clara Pizzuti, and Giandomenico Spezzano. "An ensemble-based evolutionary framework for coping with distributed intrusion detection. " Genetic Programming and Evolvable Machines, June 2010.
  17. H. G. Kayacik, A. N. Zincir-Heywood, and M. I. Heywood, "Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets", In Proceedings of the 3rd Annual Conference on Privacy, Security and Trust (PST-2005), Oct. , 2005
  18. V. Vapnik,. "Statistical Learning Theory. " Wiley, New York, 1998
  19. Vapnik V. "The nature of statistical learning theory. " New York: Springer; 1995.
  20. Kuan-Ming Lin and Chih-Jen Lin, "A Study on Reduced Support Vector Machines", IEEE Transactions On Neural Networks, VOL. 14, NO. 6, NOVEMBER 2003.
  21. R. Debnath, H. Takahashi, "SVM Training: Second-Order Cone Programming versus Quadratic programming", 2006 IEEE International Joint Conference on Neural Networks, Canada, July 16-21, 2006.
  22. Arvind Mewada, PraffulGedam, Shamaila khan,M. Udayapal reddy, " Network Intrusion Detection Using Multiclass Support Vector Machine", Special Issue of IJCCT Vol. 1 Issue 2, 3, 4; 2010 for International Conference [ACCTA-2010], 3-5 August 2010
  23. T. M. Khoshgoftaar, M. Golawala and J. Van Hulse, "An Empirical Study of Learning from Imbalanced Data Using Random Forest. " In Proceedings of the 19th. IEEE Conference on Tools with Artificial Intelligence, 2007.
  24. Vu N. P. Dao 1, Rao Vemuri, "A Performance Comparison of Different Back Propagation Neural Networks Methods in Computer Network Intrusion Detection", Differential Equations and Dynamical System 2002
  25. J. Principe, N. Euliano, W. Lefebvre, "Neural and Adaptive System – Fundamentals Through Simulations", Wiley, 2000.
  26. P. Kang, and S. Cho, "EUS SVMs: Ensemble of Under Sampled SVMs for Data Imbalance Problems. " ICONIP 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion detection classification ensemble particle swam optimization support vector machine SVM ANN RS

Powered by PhDFocusTM