Abstract
In the data driven world finding the appropriate user behavior is ambitious. Intrusion detection system is used to do such task, in most of the cases it is not accurate and time consuming process. In this approach, finding such behavior in effectively and accurately the rough set based approach and attribute scaling are used. Intrusion detection is the classification problem, it is used to differentiate between the normal and anomaly behavior accurately. In the process of evaluation all the attributes may not be involved in classification. Selecting the competent attributes from the dataset rough set based feature selection technique is adopted. The preferred attributes may not be scaled properly, scaling of the attributes improves the detection performance. In this approach, rough set based feature selection and attribute scaling are combined with classification to increasing the capability of intrusion detection and decreases the detection time.
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References
J. P. Anderson, “Computer Security Threat Monitoring and Surveillance”, Technical Report, April 1980.
Denning D, “An Intrusion-Detection Model”, IEEE Transactions on Software Engineering, Vol. SE-13, No. 2, pp. 222–232, 1987.
Lee W and Stolfo S.J, “Data Mining techniques for intrusion detection”, In: Proc. of the 7th USENIX security symposium, San Antonio, TX, 1998.
Dash M & Liu H., “Feature Selection for Classification. Intelligent Data Analysis”, Vol. 1, No. 3, pp. 131–156, 1997.
Carlos A. Catania, Calos Garcia Garino, “Automatic network intrusion detection: Current techniques and open issues”, Computers an Electrical Engineering 38, pp: 1062–1072, 2012.
Langley P, “Selection of relevant features in machine learning”, In Proceedings of the AAAI Fall Symposium on Relevance, pp: 1–5, 1994.
Pawlak Z., “Rough sets”, International Journal of Computer and Information Sciences, vol. 11, pp: 341–356, 1982.
L. Zhang, G. Zhang, L. Yu, J. Zhang, and Y. Bai, “Intrusion Detection Using Rough Set Classification”, Journal of Zheijiang University Science. 5(9), pp. 1076–1083, 2004.
Shiravi A, Shiravi H, Tavallaee M, Ghorbani AA, “Toward developing a systematic approach to generate benchmark datasets for intrusion detection”, Computer Security, Vol. 31, pp: 357–74, 2012.
Sen S, Clark JA, “Evolutionary computation techniques for intrusion detection in mobile ad hoc networks”, Computer Networks, 55, pp: 41–57, 2011.
Kwang-Kyu Seo. “A GA-Based Feature Subset Selection and Parameter Optimization of Support Vector Machine for Content – Based Image Retrieval”, Lecture Notes in Computer Science, 2007.
Cao, Peng, Dazhe Zhao, and Osmar Zaiane. “Measure optimized wrapper framework for multi-class imbalanced data learning: An empirical study”, The 2013 International Joint Conference on Neural Networks (IJCNN), 2013.
C.A Catania, C.G Garino, “Automatic network intrusion detection: Current techniques and open issues”, Computers & Electrical Engineering 38 (5), pp: 1062–1072, 2012.
L. H. Zhang, G. H. Zhang, L. Yu, J. Zhang and Y.C. Bai, “Intrusion detection using rough set classification”, Journal of Zhejiang University Science, 5(9), 1076–1086, 2004.
Lee W, Stolfo S. J., And Mok K. W, “A data mining framework for building intrusion detection models”, In Proceedings of the 1999 IEEE Symposium on Security and Privacy, 1999.
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Ravinder Reddy, R., Ramadevi, Y., Sunitha, K.V.N. (2017). Systematic Approach to Intrusion Evaluation Using the Rough Set Based Classification. In: Saini, H., Sayal, R., Rawat, S. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 8. Springer, Singapore. https://doi.org/10.1007/978-981-10-3818-1_24
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DOI: https://doi.org/10.1007/978-981-10-3818-1_24
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