The week's pick
Reseach Article
Feature or Attribute Extraction for Intrusion Detection System using Gain Ratio and Principal Component Analysis (PCA)
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 4 - Number 3 |
| Year of Publication: 2016 |
| Authors: O. Isaiah Aladesote, Agbelusi Olutola, Olasehinde Olayemi |
10.5120/cae2016652032
|
O. Isaiah Aladesote, Agbelusi Olutola, Olasehinde Olayemi . Feature or Attribute Extraction for Intrusion Detection System using Gain Ratio and Principal Component Analysis (PCA). Communications on Applied Electronics. 4, 3 ( January 2016), 1-4. DOI=10.5120/cae2016652032
Abstract
Intrusion detection systems (IDS) refer to a category of defense tools that is used to provide warnings indicating that a system is under attack or intrusion. The IDS monitors activities within a network and alerts security administrators of suspicious activities. This paper extracted significant or highly relevant features or attributes of the Knowledge Discovery and Data Mining 1999 (KDD ’99) dataset, which is a standard benchmark dataset for all intrusion problems using two features extraction techniques: Gain Ratio for discrete attributes and Principal Component Analysis (PCA) for continuous attributes. C# Programming language was used for the implementation. Also, Microsoft Excel was used to depict the result of the extraction. The result shows that thirteen (13) attributes were highly relevant and significant.
References
- Ankita G., and Richariya V. (2007): “A Layered Approach for Intrusion Detection using Meta- modelling with Classification Techniques,” International Journal of Computer Technology & Electronics Engineering (IJCTEE) Vol. 1, Issues 2.
- Asha Gowda Karegowda, A. S. Manjunati & M. A. Jayaram (2010): “Comparative Study of Attributes Selection using Gain Ratio and Correlation Based Feature Selection”, International Journal of Information Technology and Knowledge Management. Volume 2, No. 2, pp. 271 – 277, July – December 2010.
- Dharamraj R. Patil and V. P. Kshirsagar (2010): “An overview of adaboost-based NISD and Performance evaluation on NSL –KDD dataset”, International Journal of Computer Engineering and Computer Application, vol. 1, 2010.
- Flora S. T. (2009), “Network Intrusion Detection using Associative Rules,” International Journal of Recent Trends in Engineering, Vol. 2, No. 2, November 2009.
- Jaiganesh, V. and Sumathi, P. (2012): “Intrusion Detection using Kernelized Support Vector Machine with Levenberg – Marquardt Learning”, International Journal of Engineering Science and Technology (IJEST), vol. 4 No. 03, pp. 1153 – 1160, March 2012.
- Kayacik, H. G., Zincir-Heywood, A. N, and Heywood M. I. (1999): “Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD ’99 Intrusion Detection Datasets,” http://www.cs.dal.ca/projectx/
- KDD Cup 1999 Data: Available on http://kdd.ics.uci.edu/database/kddcup99/Database/kddcup99/kddcup99.html, October 2007
- Mrutyunjaya Panda and Manas Ranjan Patra (2007): “Network Intrusion Detection using Naive Bayes”, International Journal of Computer Science and Network Security (IJCSNS), Vol. 7, No. 12, December 2007.
- Rupati D, and Bhupendra V. (2010): “Feature Reduction for Intrusion Detection using Linear Discriminant Analysis”, International Journal on Computer Science and Engineering (IJCSE) Vol. 02, No. 04, 2010, 1072 – 1078.
- Shilpa, L., Joseph S., and Bhupendra V. (2010): “Feature Reduction using Principal Component Analysis for Effective Anomaly-Based Intrusion Detection on NSL-KDD,” International Journal of Engineering Science and Technology, vol. 2(6), 2010, 1970 – 1977.
- Tsai F. S., Chan C. K. (eds), Cyber Security, Pearson Education, Singapore, 2006.
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