Relevance feature selection via analysis of the KDD '99 intrusion detection dataset
DOI:
https://doi.org/10.46243/jst.2022.v7.i09.pp0135-140Keywords:
degree of dependency, rough set, relevance feature, machine learning, Intrusion detectionAbstract
The rapid development of business and othertransaction systems over the Internet makes computer securitya critical issue. In recent times, data mining and machine learning have been subjected to extensive research in intrusion detection with emphasis on improving the accuracy of detection classifier. But selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. In this paper, we presented therelevance of each feature in KDD ’99 intrusion detection dataset to the detection of each class. Rough set degree of dependency and dependency ratio of each class were employed to determine the most discriminating features for each class. Empirical results show that seven features were not relevant in the detection of any class.
References
Bace, R. and Mell, P. (2001). Intrusion Detection System, NIST Special Publications SP 800. November
Lee, W., Stolfo, S.J. and Mok, K. (1999). Data Mining in work flow environments: Experiments in intrusion detection. In Proceedings of the 1999 Conference on Knowledge Discovery and Data Mining
Mukkamala, S., Janoski, G., Sung, A. (2002). Intrusion detection using neural networks and support vector machines. In: Proceedings of IEEE International Joint Conference on Neural Networks, pp. 1702–1707
Byunghae, C., kyung, W.P. and Jaittyun, S. (2005) Neural Networks Techniques for Host Anomaly Intrusion Detection using Fixed Pattern Transformation in ICCSA. LNCS 3481. 254-263
Ajith, A., Ravi J., Johnson T. and Sang, Y.H. (2005). D-SCIDS: Distributed soft computing intrusion detection system, Journal of Network and Computer Applications, Elsevier, pp. 1-19
Susan M. B. and Rayford B.V. (2000). Intrusion detection via fuzzy data mining, Proceedings of the 12th Annual Canadian Information Technology Security Symposium, Ottawa, Canada, June 19-23, 2000, PP.109-122
Quinlan, J.L. (1993) C4.5 Program for Machine Learning, Morgan Kaufmam Publishers, Inc
Pavel L., Patrick D., Christia S. and Konrad R. (2005). Learning Intrusion Detection: Supervised or Unsupervised?, International Conference on image analysis and processing, (ICAP), Italie, 2005 (3617) pp. 50-57
Zhang, L., Zhang, G., YU, L., Zhang, J. and Bai, Y. (2004) Intrusion detection using Rough Set Classification, Journal of Zhejiang University SCIENCE, 5(9):1076-1086
Published
How to Cite
Issue
Section
