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Feature Optimization and Performance Improvement of a Multiclass Intrusion Detection System using PCA and ANN

by Ravi Kiran Varma.p, V. Valli Kumari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 13
Year of Publication: 2012
Authors: Ravi Kiran Varma.p, V. Valli Kumari
10.5120/6321-8668

Ravi Kiran Varma.p, V. Valli Kumari . Feature Optimization and Performance Improvement of a Multiclass Intrusion Detection System using PCA and ANN. International Journal of Computer Applications. 44, 13 ( April 2012), 4-9. DOI=10.5120/6321-8668

@article{ 10.5120/6321-8668,
author = { Ravi Kiran Varma.p, V. Valli Kumari },
title = { Feature Optimization and Performance Improvement of a Multiclass Intrusion Detection System using PCA and ANN },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 13 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 4-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number13/6321-8668/ },
doi = { 10.5120/6321-8668 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:35:56.649386+05:30
%A Ravi Kiran Varma.p
%A V. Valli Kumari
%T Feature Optimization and Performance Improvement of a Multiclass Intrusion Detection System using PCA and ANN
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 13
%P 4-9
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There are several bottle necks in the process of high speed intrusion detection, of which large dimensionality is one of the major problem. We have employed the Principal Component Analysis (PCA) algorithm to handle this problem, through which we have improved the performance of the Artificial Neural Network (ANN) classifier for intrusion detection. With the help of PCA we were able to identify the top 15 out of 41 features among the feature set of KDD cup 1999 data set, and noticed an improvement of over 62% in the training time of ANN. The Multi Layer Perceptron Neural Network improved the accuracy even after the feature reduction.

References
  1. Ravi Kiran Varma. P, V. Valli Kumari, VVS. Prasanna, "Heuristic approach to improve the performance of ANN based Intrusion Detection System ", Proceedings of the 3rd IEEE International Conference on Signal Processing Systems, August 2011, Yantai, China, pp 071-076, 2011
  2. Kachurka P, Golovko V, "Neural Network Approach to real time network intrusion detection and recognition", IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems, September 2011, Czech Republic, pp 393-397, 2011
  3. Amza C, Leordeanu C, Cristea V, "Hybrid Network Intrusion Detection", Proceedings of the 2011 IEEE International Conference on Intelligent Computer Communication and Processing, pp 503-510, Romania, Aug 2011.
  4. Fariba Haddadi, Sara khanchi, Mehran Shetabi, Vali Derhami "Intrusion detection and attack classification using Feed-Forward Neural Network", Proceedings of the Second IEEE International Conference on Computer and Network Technology, Thailand, pp 262-266, April 2010
  5. T. Petreus, CE Cotrutz, M. Neamtu, E. C. Buruiana, P. D. Sirbu, A. Neamtu, "Understanding the dynamics-activity relationship in metalloproteases : Idea for new inhibition strategies", 2010 IEEE International Conference on Advanced Technologies for Enhancing Quality of Life, pp 83-86, Romania, Dec 2010.
  6. Jason L Wright, Milos Mamic, "The Analysis of Dimensionality Reduction techniques in Cryptographic Object Code Classification", IEEE International Conference on Human System Interaction,pp 157-162, Poland, May 2010.
  7. Leila Mechtri, Fatiha Djemili Tolba, Nacira Ghoualmi, "Intrusion Detection Using Principal Component Analysis", IEEE 2nd International Conference on Engineering Systems Management and its Applications, pp 1-6, France, Aug 2010.
  8. Varun Chandola, Arindam Banerjee, Vipin Kumar, "Anomaly Detection: A Survey", ACM Computing Surveys, Vol. 41, No. 3, Article 15, July 2009.
  9. Yana Demidova, Maksym Ternovoy, "Neural Network Approach of Attack's Detection In the Network Traffic", The IXth International Conference "The Experience of Designing and Application of CAD Systems in Microelectronics" Feb 2007 IEEE, Ukraine, pp128-129, 2007
  10. D. E. Denning, "An intrusion detection model," IEEE Transactions on Software Engineering, vol. 13, no. 2, pp. 222–232, 1987.
  11. . J. Ryan, M. Lin and R. Miikkulainen, Intrusion Detection with Neural Networks, AI Approaches to Fraud Detection and Risk Management: Papers from the 1997 AAAI Workshop, Providence, RI, pp. 72-79, 1997.
  12. K. Fox, R. Henning, J. Reed and R. Simonian, A Neural network approach towards intrusion detection, Proceedings of ACM 13th National Computer Security Conference, Baltimore, MD, pp. 125-134, 1990.
  13. James Cannady, Artificial neural networks for misuse detection, Proceedings of the 1998 National Information Systems Security Conference (NISSC'98), Arlington, VA, 1998
  14. D. Nagaraju, P. Srinivasulu, V. Valli Kumari, A. Govardhan, "Outlier detection as network intrusion detection system using multilayered framework" proceedings of the Springer CCSIT 2011, pp 101-111, 2010.
  15. H. Debar, M. Becker, and D. Siboni, A neural network component for an intrusion detection system, Proceedings of 1992 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, California, pp. 240 – 250, 1992.
  16. M. Moradi, Mohammad Zulkernine. A Neural Network Based System for Intrusion Detection and Classification of Attacks. Proceedings of 2004 IEEE International Conference on Advances in Intelligent Systems - Theory and Applications, Luxembourg-Kirchberg, Luxembourg, 2004.
  17. John McHugh, Alan Christie, Julia Allen, "Defending Yourself: The role of Intrusion Detection Systems", IEEE Software, vol 17 no. 5 pp 42-51, Sep 2000.
  18. Iftikhar Ahmad, Azween B Abdullah, Abdullah S Alghamdi "Application of artifitial neural networks in detection of DOS attacks", Proceedings of ACM SIN'09, North Cyprus, Turkey, 2009.
  19. S. Mukkamala, G. Janoski, A. H. Sung, "Intrusion Detection Using Neural Networks and Support Vector Machines", Proceedings of IEEE International Joint Conference on Neural Networks (Hawaii, May 2002) pp 1702-1707, 2002
  20. Amit kumar Choudary, Akhilesh Swarup, "Neural Network Approach for Intrusion Detection", proceedings of the 2009 International Conference of Interaction Sciences, Seoul, Korea, 2009
  21. Srinivas Mukkamala, Intrusion detection using neural networks and support vector machine, Proceedings of the 2002 IEEE International Honolulu, HI, 2002.
  22. KDD 1991 datasets. The UCI LDD Archive, http://kdd. ics. uci. edu/databases/kddcup99/kddcup99. html, Irvine, CA, USA, 1999.
  23. J. P Anderson. Computer Security Threat Monitoring and Surveillance. Technical report, James P Anderson Co. , Fort Washington, Pennsylvania, April 1980.
  24. Solomon Raju Kota, J. L. Raheja, Ashutosh Gupta, Archana Rathi, Shashikant Sharma, "Principal Component Analysis for Gesture Recognition using System C", 2009 IEEE International Conference on Adcances in Recent Technologies in Communication and Computing", pp 732-737, India, October 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Mlp Neural Networks Principal Component Analysis Intrusion Detection System.

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