Feature Selection plays the very important role in Intrusion Detection System. One of the major challenge these days is dealing with large amount of data extracted from the network that needs to be analyzed. Feature Selection helps in selecting the minimum number of features from the number of features that need more computation time, large space, etc. This paper, analyzed different feature selection technique on the NSL-KDD dataset by using C45 classifier, compared these techniques by various performance metrics like classifier accuracy, number of features selected, a list of features selected, elapsed time.

1. Oreski, D. , & Novosel, T. Comparison of Feature Selection Techniques in Knowledge Discovery Process.
2. Kaur, R. , Kumar, G. , & Kumar, K. A Comparative Study of Feature Selection Techniques for Intrusion Detection.
3. Barot, V. , Chauhan, S. S. , & Patel, B. (2014). Feature Selection for Modeling Intrusion Detection
4. Shah, B. , & Trivedi, B. H. (2015, February). Reducing Features of KDD CUP 1999 Dataset For Anomaly Detection Using Back Propagation Neural Network. In Advanced Computing & Communication Technologies (ACCT), 2015 Fifth International Conference on (pp. 247-251). IEEE.
5. Derrac, J. , et al. "KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. " (2015).
6. Kumar, K. , Kumar, G. , & Kumar, Y. (2013). Feature Selection Approach for Intrusion Detection System. International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 2(5).
7. Beniwal, S. , & Arora, J. (2012). Classification and feature selection techniques in data mining. International Journal of Engineering Research & Technology (IJERT), 1(6).
8. Shardlow, Matthew. "An Analysis of Feature Selection Techniques. "
9. Kira, K. , & Rendell, L. A. (1992). A practical approach to feature selection. InProceedings of the ninth international workshop on Machine learning (pp. 249-256).
10. Robnik-Å ikonja, M. , & Kononenko, I. (1997, July). An adaptation of Relief for attribute estimation in regression. In Machine Learning: Proceedings of the Fourteenth International Conference (ICML'97)
11. H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502.
12. H. Liu, R. Setiono. A Probabilistic Approach to Feature Selection: A Filter Solution. 13th International Conference on Machine Learning (ICML96 ). Bari (Italy, 1996) 319-327.
13. H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502.
14. H. Liu, R. Setiono. Feature Selection and Classification: A Probabilistic Wrapper Approach. 9th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA-AIE'96). Fukuoka (Japon, 1996) 419-424.
15. H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502.

Intrusion Detection System Feature Selection Nsl-kdd Data Mining Classification.