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Reseach Article

Effective Fast and Fuzzy Art Map Performance to Detect Intrusion

Published on December 2015 by Swati A Sonawale, Roshani Ade
National Conference on Advances in Computing
Foundation of Computer Science USA
NCAC2015 - Number 3
December 2015
Authors: Swati A Sonawale, Roshani Ade
da4263bf-565e-4e77-b4d9-76c4555b8cde

Swati A Sonawale, Roshani Ade . Effective Fast and Fuzzy Art Map Performance to Detect Intrusion. National Conference on Advances in Computing. NCAC2015, 3 (December 2015), 29-33.

@article{
author = { Swati A Sonawale, Roshani Ade },
title = { Effective Fast and Fuzzy Art Map Performance to Detect Intrusion },
journal = { National Conference on Advances in Computing },
issue_date = { December 2015 },
volume = { NCAC2015 },
number = { 3 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 29-33 },
numpages = 5,
url = { /proceedings/ncac2015/number3/23375-5041/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computing
%A Swati A Sonawale
%A Roshani Ade
%T Effective Fast and Fuzzy Art Map Performance to Detect Intrusion
%J National Conference on Advances in Computing
%@ 0975-8887
%V NCAC2015
%N 3
%P 29-33
%D 2015
%I International Journal of Computer Applications
Abstract

Great research work have been conducted towards Intrusion Detection Systems (IDSs) as well as feature selection. Feature selection applications have a great influence on decreasing development lead times and increasing product quality as well as proficiency. IDS guards a system from attack, misuse, and compromise. It can also screen network action. Network traffic observing and extent is progressively regarded as a key role for understanding and improving the performance and security of our cyber infrastructure. By using IDS attack can be detected in system as info is vital strength for every business. It can cause millions of harm within a few seconds. Security is important factor because reputation of business depends on it. So timely detection of intrusion is important so that preventive actions can be taken. IDS framework has been proposed by using fuzzy feature selection method with ARTMAP. It has been observed that the proposed framework gives better accuracy in less time as compared to methods in literature.

References
  1. A Fast clustering based feature subset selection algorithm for high dimensional data Qinbao Song, Jingjie Ni, and Guangtao Wang ieee transactions on knowledge and data engineering, VOL. 25, NO. 1, Jan 2013.
  2. Z. Zhao and H. Liu, Spectral Feature Selection for Data Mining, USA: Chapman and Hall-CRC, 2012.
  3. I. . Jolliffe, Principal Component Analysis, USA: Springer, 2002.
  4. X. He and P. Niyogi, "Locality preserving projections," in Proc. NIPS, 2004.
  5. M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," in Proc. NIPS, 2002.
  6. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res. , vol. 3, pp. 1157–1182, Mar. 2003.
  7. J. G. Dy and C. E. Brodley, "Feature selection for unsupervised learning," J. Mach. Learn. Res. , vol. 5, Aug. 2004, pp. 845–889.
  8. M. Robnik-Sikonja and I. Kononenko, "Theoretical and empirical analysis of relief and relieff," Mach. Learn. , vol. 53, no. 1–2, pp. 23–69, 2003.
  9. L. Yu and H. Liu, "Efficient feature selection via analysis of relevance and redundancy," J. Mach. Learn. Res. , vol. 5, Oct. 2004, pp. 1205–1224.
  10. Z. Zhao and H. Liu, "Spectral feature selection for supervised and unsupervised learning," in Proc. 24th Int. Conf. Mach. Learn. , Corvallis, OR, USA, 2007.
  11. X. He, D. Cai, and P. Niyogi, "Laplacian score for feature selection," in Proc. NIPS, Vancouver, Canada, 2005.
  12. L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt, "Feature selection via dependence maximization," J. Mach. Learn. Res. , vol. 13, no. 1, Jan. 2012, pp. 1393–1434.
  13. Z. Zhao and H. Liu, "Semi-supervised feature selection via spectral analysis," in Proc. SIAM Int. Conf. Data Mining, Tempe, AZ, USA, 2007, pp. 641–646.
  14. D. Zhang, Z. Zhou, and S. Chen, "Semi - supervised Dimensionality reduction ," in Proc. SIAM Int. Conf. Data Mining, Pittsburgh, PA, USA, 2007.
  15. O. Chapelle, B. SchÄolkopf, and A. Zien, editors. Semi- Supervised Learning. MIT Press, C ambridge, 2006.
  16. S. BASU, M . BILENKO, AND R . MOONEY, A probabilistic framework for semi-supervised clustering, in KDD'04, Seattle, WA, 2004, pp. 59–68.
  17. U. Brefeld, T. G¨A RTNER, T. SCHEFFER, AND S. WROBEL, Efficient co-regularized least squares regression, in ICML'06, Pittsburgh, PA, 2006, pp. 137–144.
  18. K. WAGSTAFF, C. CARDIE, S. ROGERS, AND S. SCHROEDL, Constrained k-means clustering with background knowledge, in ICML'01, Williamstown, MA, 2001, pp. 577–584.
  19. T. ZHANG AND R. K. ANDO, Analysis of spectral kernel design based semi-supervised learning, in NIPS 18, MIT Press, Cambridge, MA, 2006, pp. 1601–1608.
  20. Z. -H. ZHOU AND M. LI, Semi-supervised learning with co-training, in IJCAI'05, Edinburgh, Scotland, 2005.
  21. X. ZHU, Semi-supervised learning literature survey, Tech. Report 1530, Department of Computer Sciences, University of Wisconsin at Madison, Madison, WI, 2006. http://www. cs. wisc. edu/»jerryzhu/pub/ssl survey. pdf.
  22. J. G. Dy and etal. Unsupervised feature selection applied to content -based retrieval of lung images. Transactions on pattern Analysis and Machine Intelligence, 25(3):373-378, 2003.
  23. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. Least Angle regression. Annals of Statistics, 32:407–49, 2004.
  24. G. Forman. An extensive empirical study of feature Selection metrics for text classification. Journal of Machine Learning Research, 3:1289–1305, 2003.
  25. Swati Sonawale and Roshani Ade "Review on intrusion Detection using fuzzy ARTMAP with feature selection Technique "International journal of science and research (IJSR), ISSN (Online):2319-7064, vol-3 ,Issue 11,Nov 2014.
  26. Swati Sonawale and Roshani Ade "Intrusion detection system-via fuzzy ARTMAP in addition with advance semi supervised feature selection" International journal of data mining and knowledge management process(IJDKP),vol. 5,no. 3,May 2015.
  27. Swati Sonawale and Roshani Ade " Dimensionality Reduction: An effective technique for feature selection", International journal of computer application:0975-8887, Vol. 117. no 3,pp 18-23,May 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Intrusion Detection Redundancy Fuzzy Artmap.