Random Articles
Reseach Article
Feature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 78 - Number 4 |
| Year of Publication: 2013 |
| Authors: Zahra Karimi, Mohammad Mansour Riahi Kashani, Ali Harounabadi |
10.5120/13478-1164
|
Zahra Karimi, Mohammad Mansour Riahi Kashani, Ali Harounabadi . Feature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods. International Journal of Computer Applications. 78, 4 ( September 2013), 21-27. DOI=10.5120/13478-1164
Abstract
Intrusion detection is a crucial part for security of information systems. Most intrusion detection systems use all features in their databases while some of these features may be irrelevant or redundant and they do not contribute to the process of intrusion detection. Therefore, different feature ranking and feature selection techniques are proposed. In this paper, hybrid feature selection methods are used to select and rank reliable features and eliminate irrelevant and useless features to have a more accurate and reliable intrusion detection process. Due to the low cost and low accuracy of filtering methods, a combination of these methods could possibly improve their accuracy by a reasonable cost and create a balance between them. In the first phase, two subsets of reliable features are created by application of information gain and symmetrical uncertainty filtering methods. In the second phase, the two subsets are merged, weighted and ranked to extract the most important features. This feature ranking which is done by the combination of two filtering methods, leads to higher the accuracy of intrusion detection. KDD99 standard dataset for intrusion detection is used for experiments. The better detection rate obtained in proposed method is shown by comparing it with other feature selection methods that are applied on the same dataset.
References
- Gilad-Bachrach, R. , Navot, A. , and Tishby, N. , 2004. Margin Based Feature Selection - theory and Algorithms, Proceedings of the twenty-first international conference on Machine learning (ICML), Page(s) 43–50.
- Wang, H. , Khoshgoftaar, T. M. , and Gao, k. , 2010. Ensemble Feature Selection Technique for Software Quality Classification, In Proceedings of the 22nd International Conference on Software Engineering and Knowledge Engineering, Page(s) 215–220.
- Liu, H. , Li, J. , and Wong, L. , 2002. A Comparative Study on Feature Selection and Classification Methods Using Expression Profiles and Proteomic Patterns, Genome Informatics, Volume 13, Page(s) 51–60.
- Ruiz, R. , Aguilar-Ruiz, J. S. , Santos, J. C. , and Diaz- Diaz, N. , 2005. Analysis of Feature Rankings for Classification, In Proceedings of the 6th International Symposium on Intelligent Data Analysis, Volume 3646, Page(s) 362–372.
- Hall M. A. , and Holmes, G. , 2003. Benchmarking Attribute Selection Techniques for Discrete Class Data Mining, IEEE Transactions on Knowledge and Data Engineering, Volume 15, No. 3, Page(s) 1437–1447.
- John, G. H. , Kohavi, R. , and Pfleger, K. , 1994. Irrelevant Features and the Subset Selection Problem, Proceedings of the Eleventh International Conference, Page(s) 121-129, Morgan Kaufmann.
- Biesiada, J. , and Duch, W. , 2005. Feature Selection for High-Dimensional Data: A Kolmogorov-Smirnov Correlation-Based Filter Solution, in Proceedings of the 4th International Conference on Computer Recognition Systems.
- Witten, I. H. , and Frank, E. , 2005. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems).
- Fayyad U. M. , and Irani, K. B. , 1992. On the Handling of Continuous-Valued Attributes in Decision Tree Generation, Machine Learning, Page(s) 87–102.
- Yang Y. , and Pedersen, J. O. , 1997. A Comparative Study on Feature Selection in Text Categorizatio, 14th International Conference on Machine Learning, Page(s) 412–420.
- Scarfone, K. , and Mell, P. , 2007. Guide to Intrusion Detection and Prevention Systems (IDPS), Computer Security Resource Center (National Institute of Standards and Technology).
- Intrusion Detection System, http://www. webopedia. com/Term/I/intrusion_detection_system. html, [Accessed:Jan. 10, 2013].
- Verwoerd, T. , and Hunt, R. , 2002. Intrusion Detection Techniques and Approaches, Computer Communications. Volume 25, Page(s) 1356-1365.
- Chou, T. S. , Yen K. K. , and Luo, J. , 2007. Network Intrusion Detection Design Using Feature Selection of Soft Computing Paradigms, International Journal of Computational Intelligence, Page(s) 196-208, Volume 4, No. 3.
- Novakovic, J. , Strabac, P. , and Bulatovic, D. , 2011. Toward Optimal Feature Selection Using Ranking Method and Classification Alghorithms, Yugoslav Journal of Operations Research, Volume 21, No. 1, Page(s) 119-135.
- Hall, M. A. , and Smith, L. A. , 1998. Practical Feature Subset Selection for Machine Learning, Computer Science Proceedings of the 21st Australasian Computer Science Conference ACSC, Page(s) 181-191.
- Knowledge Discovery in Databases DARPA archive Task Description. KDDCUP 1999 DataSet, http://kdd. ics. uci. edu/databases/kddcup99/kddcup99. html, [Accessed: Dec. 06, 2012].
Index Terms
Keywords