CFP last date
22 April 2024
Reseach Article

Article:Discriminant Analysis based Feature Selection in KDD Intrusion Dataset

by Dr.S.Siva Sathya, Dr. R.Geetha Ramani, K.Sivaselvi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 11
Year of Publication: 2011
Authors: Dr.S.Siva Sathya, Dr. R.Geetha Ramani, K.Sivaselvi
10.5120/3938-5527

Dr.S.Siva Sathya, Dr. R.Geetha Ramani, K.Sivaselvi . Article:Discriminant Analysis based Feature Selection in KDD Intrusion Dataset. International Journal of Computer Applications. 31, 11 ( October 2011), 1-7. DOI=10.5120/3938-5527

@article{ 10.5120/3938-5527,
author = { Dr.S.Siva Sathya, Dr. R.Geetha Ramani, K.Sivaselvi },
title = { Article:Discriminant Analysis based Feature Selection in KDD Intrusion Dataset },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 11 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume31/number11/3938-5527/ },
doi = { 10.5120/3938-5527 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:17:52.272041+05:30
%A Dr.S.Siva Sathya
%A Dr. R.Geetha Ramani
%A K.Sivaselvi
%T Article:Discriminant Analysis based Feature Selection in KDD Intrusion Dataset
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 11
%P 1-7
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intrusion detection system (IDS) plays a major role in providing network security by analyzing the network traffic log and classifying the records as attack or normal behavior. Generally, as each log record is characterized by a large set of features, an Intrusion Detection System consumes large computational power and time for the classification process. Hence, feature reduction becomes mandatory before attack classification for any IDS. Discriminant analysis is a technique which can be used for selecting important features in large set of features. In this paper, important features of KDD Cup ‘99 attack dataset are obtained using discriminant analysis method and used for classification of attacks. The results of discriminant analysis show that classification is done with minimum error rate with the reduced feature set.

References
  1. J. H. Güneş Kayacýk, A. Nur Zincir-Heywood, Malcolm I. Heywood. Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets.
  2. Jianhua Sun, Hai Jin, Hao Chen, Zongfen Han, and Deqing Zou. 2003. A Data Mining Based Intrusion Detection Model. Lecture Notes in Computer Science. Intelligent Data Engineering and Automated Learning. Springer publications. Volume 2690, 677-684.
  3. Khaled Labib. 2004. Computer Security and Intrusion Detection. ACM Crossroads. Volume 11(1): 2.
  4. Scarfone, Karen; Mell, Peter. 2007. Guide to Intrusion Detection and Prevention Systems (IDPS). Computer Security Resource Center (National Institute of Standards and Technology).
  5. Theuns Verwoerd, Ray Hunt. 2002. Intrusion detection techniques and approaches. Computer Communications. Volume 25, 1356-1365.
  6. Seema Jaggi and P.K.Batra, “SPSS: An Overview”.
  7. SPSS Inc., “SPSS 13.0 Base User’s Guide”.
  8. Knowledge discovery in databases DARPA archive and Task Description. http://www.kdd.ics.uci.edu/databases/kddcup99/task.html.
  9. The 1998 intrusion detection off-line evaluation plan, MIT Lincoln Lab, Information Systems Technology Group.http://www.11.mit.edu/IST/ideval/docs/1998/id98-eval-11.txt.
  10. S. Hettich, S.D. Bay. 1999. The UCI KDD Archive. Irvine, CA: University of California, Department of Information and Computer Science. http://kdd.ics.uci.edu.
  11. David W. Stockburger, Multivariate Statistics: Concepts, Models, and Applications. http://www.psychstat.missouristate.edu/multibook/mlt03.htm.
  12. G. David Garson, Discriminant Function Analysis. 2008. http://www2.chass.ncsu.edu/garson/pa765/discrim.htm.
  13. Intrusion Detection System. .http://www.webopedia.com/TERM/I/intrusion_detection_system.html.
  14. Midori Asak a, Takefumi Onabura, T adashi Inoue, Shigeki Goto. 2002. Remote Attack Detection Method in IDA: MLSI-Based Intrusion Detection using Discriminant Analysis. Proceedings of the 2002 Symposium on Applications and the Internet (SAINT.02), IEEE.
  15. A. Aarabi, F. Wallois, R. Grebe. 2005. Feature Selection Based on Discriminant and Redundancy Analysis Applied to Seizure Detection in Newborn. Proceedings of the 2nd IEEE EMBS. Conference on neural Engineering, Arlington, Virginia.
  16. Mohamed Elgendi, Mirjam Jonkman, Friso De Boer. 2008. PREMATURE ATRIAL COMPLEXES DETECTION USING THE FISHER LINEAR DISCRIMINANT. Proceedings of the 7th IEEE International Conference on Cognitive Informatics (ICCI'08).
  17. Kun-Ming Yu and Ming-Feng Wu and Wai-Tak Wong. 2008. Protocol-Based Classification for Intrusion Detection. WSEAS TRANSACTIONS on COMPUTER RESEARCH. Volume 3(3).
  18. Zhiyuan Tan, Aruna Jamdagni, Xiangjian He, Priyadarsi Nanda. 2010. Network Intrusion Detection Based on LDA for Payload Feature Selection. IEEE Globecom 2010 Workshop on web and pervasive security.
  19. Fatemeh Amiri, MohammadMahdi Rezaei Yousefi , Caro Lucas , Azadeh Shakery NasserYazdani. 2011. Mutual information-based feature selection for intrusion detection systems. Journal of Network and Computer Applications. Volume 34, 1184–1199.
Index Terms

Computer Science
Information Sciences

Keywords

Discriminant analysis KDD Cup ’99 attack dataset classification features relevance minimum error rate SPSS