The week's pick
Random Articles
Reseach Article
Intrusion Detection using Supervised Learning with Feature Set Reduction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 33 - Number 6 |
| Year of Publication: 2011 |
| Authors: Yogendra Kumar Jain, Upendra |
10.5120/4025-5738
|
Yogendra Kumar Jain, Upendra . Intrusion Detection using Supervised Learning with Feature Set Reduction. International Journal of Computer Applications. 33, 6 ( November 2011), 22-31. DOI=10.5120/4025-5738
Abstract
Intrusion detection systems intend to recognize attacks with a low false positive rate and high detection rate. Many feature selection methods introduced to eliminate redundant and irrelevant features, because raw features may abbreviate accuracy or robustness of classification. In this paper we are proposing the information gain technique for the selection of the features. A feature with the highest information gain is the criteria for the selection of the features. We reduced the features of the data set than run the algorithm. Result show that drastically decreased in learning time of the algorithm without compromising the accuracy which is desirable for good IDS.We analyse two learning algorithms (NB and BayesNet) for the task of detecting intrusions and compare their relative performances. We comment on the suitability of the BayesNet algorithm for the intrusion detection task based on its high accuracy and high true positive rate. We finally state the usefulness of machine learning to the field of intrusion detection.
References
- R.C. Staudemeyer, Prof. C.W. Omlin, “Feature Set Reduction for Automatic Network Intrusion Detection with Machine Learning”, Max-Born- Institute for Nonlinear Optics and Short Pulse Spectroscopy. 2009.
- Knowledge Discovery in Databases DARPA archive. Task Description.KDDCUP 1999 DataSet http://www.kdd.ics.uci.edu/databases/kddcup99/task.htm
- Pingchuan Ma,” Log Analysis-Based Intrusion Detection via Unsupervised Learning” Master of Science, School of Informatics, University of Edinburgh,2003.
- James P. Anderson, “Computer security threat monitoring and surveillance,” Technical Report 98-17, James P.Anderson Co., Fort Washington, Pennsylvania, USA, April 1980
- Dorothy E. Denning, “An intrusion detection Model,” IEEE Transaction on Software Engineering”, SE-13(2), 1987, pp. 222-232.
- M. Bahrololum, E. Salahi and M. Khaleghi, “Machine Learning Techniques for feature Reduction in Intrusion Detection Systems: A Comparison” 2009 Fourth International Conference on Computer Science.
- Meng Jianliang, Shang Haikun, “The application on intrusion detection based on K Means cluster algorithm” International Forum on Information Technology and Application, 2009.
- Li Tian, Wang Jianwen, “Research on Network Intrusion Detection System Based on Improved K-means Clustering Algorithm” International Forum on Computer Science Technology and Applications, 2009.
- Gary Stein, Bing Chen,“ Decision Tree Classifier for network intrusion detection with GA based feature selection”, University of Central Florida. Proceedings of 43rdannual Southeast regional Conference. Volume-2 ,2005.
- Rung Ching Chen, Kai Fan Cheng and Chia – Fen Hsieh,“Using rough set and support vector machine for network intrusion detection” International Journal of Network Security and Its Application (IJNSA),Vol 1, No 1, April 2009.
- Lin Ni , Hong Ying Zheng “ An Unsupervised Intrusion Detection Method Combined Clustering with Chaos Simulated Annealing” Proceeding of the Sixth International on Machine Learning and Cybernetics, Hong Kong, 19-22, August 2007.
- Jiong Zhang and Mohhammad Zulkernine,“ Anomaly based Network Intrusion detection with Unsupervised outlier detection”School of Computing Queen’s University, Kingston,Ontario,Canada.IEEE International Conference ICC 06,Volume-9, 11-15 June 2006.pp 2388-2393.
- Cuixiao Zhang, Guobing Zhang, Shanshan Sen, “A mixed unsupervised clustering based Intrusion detection model” Third International Conference on Genetic and Evolutionary Computing, 2009.
- Juan Wang, Quren Yang and Dasen Ren , “ An intrusion detection algorithm based on decision tree technology” Asia Pacific Conference based on Information Processing,2009.
- Yan Luo and Jeffrey J.P. Tsai, “A Framework for Extrusion Detection Using Machine Learning”11th IEEE Symposium on Object Oriented Real-Time Distributed computing (ISORC),2008.
- Reza Sadoddin and Ali A. Ghorbani, “A Comparative Study of Unsupervised Machine Learning and Data Mining Techniques for Intrusion Detection” Springer-Verlag Berlin Heidelberg, 2007.
- Anshu Veda “Intrusion Detection Using Data mining Techniques” Report IIT Bombay2006.
- Khalid Alsubhi,Nizar Bouabdallah,Raouf Boutaba “Performance Analysis is Intrusion Detection and Prevention System”12th IFIP/ IEEE International Symposium on Intergrated Network Management 2011.
- Dewan Md. Farid, Nouria Harbi, and Mohammad Zahidur Rahman “Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection,” International Journal of Network Security & Its Applications, Vol. 2, No. 2, April2010, pp. 12-25.
- Hongwei Gao,Dingju Zhu, Xiaomin Wang “A Parallel Clustering Ensemble algorithm for Intrusion Detection System ”2010. Ninth IEEE International Symposium on Distributed Computing and Applications to Business. Engineering and Science.
- Shaohua Teng, Hongle Du Wei Zhang, Xiufen Fu “A Cooperative Network Intrusion Detection Based on Heterogeneous Distance Function Clustering”2010 ,14th IEEE International Conference on Computer Supported Cooperative Work in Design.
Index Terms
Keywords