The week's pick
Random Articles
Reseach Article
An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL-KDD Data Set
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 99 - Number 15 |
| Year of Publication: 2014 |
| Authors: Akhilesh Kumar Shrivas, Amit Kumar Dewangan |
10.5120/17447-5392
|
Akhilesh Kumar Shrivas, Amit Kumar Dewangan . An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL-KDD Data Set. International Journal of Computer Applications. 99, 15 ( August 2014), 8-13. DOI=10.5120/17447-5392
Abstract
Information security is extremely critical issues for every organization to protect information from unauthorized access. Intrusion detection system has one of the important roles to prevent data or information from malicious behaviours. Basically Intrusion detection system is a classifier that can classify the data as normal or attacks. In this research paper, we have proposed ANN-Bayesian Net-GR technique that means ensemble of Artificial Neural Network (ANN) and Bayesian Net with Gain Ratio (GR) feature selection technique. We have applied various individual classification techniques and its ensemble model on KDD99 and NSL-KDD data set to check the robustness of model. Due to irrelevant features in data set, also applied Gain Ratio feature selection technique on best model. Finally our proposed model produces highest accuracy compare to others.
References
- Pal, M. 2007. Ensemble learning with decision tree for remote sensing classification. World Academy of Science, Engineering and Technology , Vol. 36, pp. 258-260.
- Pujari, A. K. 2001. Data mining techniques. Universities Press (India) Private Limited, Fourth Edition.
- Han, J. and Kamber, M. 2006. Data Mining Concepts and Techniques. Morgan Kaufmann, Second Edition.
- UCI Machine Learning Repository of machine learning databases 2010. University of California, school of Information and Computer Science, Irvine. C. A. web site: http://www. ics. uci. edu/~mlram,?ML. Repositary. html. Last accessed (Oct 2013).
- Koc, L. , Thomas A. M. and Sarkani S. 2012. A network intrusion detection system based on hidden Naive bayes multiclass classifier . Journal of Expert system with applications, Vol. 39, pp. 13492-13500.
- Li, Y. ,Xia J. , Zhang S. , Yan J. , Ai X. and Dai K. 2012. An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert systems with Applications, Vol. 39, pp. 424-430.
- Altwaijry, H. , and Algarny S. 2012. Bayesian based intrusion detection system . Journal of king saud University-computer and information sciences. Vol. 24, pp. 1-6.
- Chung, Y. Y. and Wahid N. 2012. A hybrid network intrusion detection system using simplified swarm optimization (SSO). Applied soft computing, Vol. 12 , pp. 3014-3022.
- Amira Sayed A. Aziz, Mostafa A. Salama, Hassanien A. E. and Sanaa El-Ola Hanafi 2012. Artificial Immune System Inspired Intrusion Detection System Using Genetic Algorithm. Informatica, Vol. 36, pp. 347–357.
Index Terms
Keywords