A Hybrid Approach based on Classification and Clustering for Intrusion Detection System
Автор: Jasmeen K. Chahal, Amanjot Kaur
Журнал: International Journal of Mathematical Sciences and Computing(IJMSC) @ijmsc
Статья в выпуске: 4 vol.2, 2016 года.
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Computer security plays an important role in everybody's life. Therefore, to protect the computer and sensitive information from the untrusted parties have great significance. Intrusion detection system helps us to detect these malicious activities and sends the reports to the administration. But there is a problem of high false positive rate and low false negative rate. To eliminate these problems, hybrid system is proposed which is divided into two main parts. First, cluster the data using K-Mean algorithm and second, is to classify the train data using Adaptive-SVM algorithm. The experiments is carried out to evaluate the performance of proposed system is on NSL-KDD dataset. The results of proposed system clearly give better accuracy and low false positive rule and high false negative rate.
Intrusion Detection System, high false positive rate, false negative rate, K-Mean, Adaptive-SVM, NSL-KDD
Короткий адрес: https://sciup.org/15010291
IDR: 15010291
Список литературы A Hybrid Approach based on Classification and Clustering for Intrusion Detection System
- L. Dhanabal, S.P. Shantharajah, "A study of NSL-KDD Dataset for Intrusion Detection System based on Classification Algorithms", International Journal of Advanced Research in Computer and Communication Engineering, Vol.4, Issue 6, pp. (446-452), June 2015.
- S. Duque, N.B Omar, "Using data Mining Algorithms for Developing a Model for Intrusion Detection System (IDS)", Proceedings of Science direct: Procedia Computer Science 61, pp. (46-51), 2015.
- B. Sharma and H. Gupta, "A design and Implementation of Intrusion Detection System by using Data Mining", IEEE Fourth International Conference on Communication Systems and Network Technologies, pp.700-704, 2015.
- U. Ravale, M. marathe, P. Padiya, "Feature Selection based Hybrid Anomoly Intrusion Detection System using K Means and RBF Kernal Function", Proceedings of Science Direct: International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 428-435, 2015.
- W. C. Lin, S. W. Ke, C. F. Tsai, "CANN: An intrusion detection system based on combining cluster centers and nearest neighbors", Proceedings of Science direct: Knowledge-Based Systems, pp. 13-21, 2015.
- J. Haque, K.W. Magld, N. Hundewale, "An Intelligent Approach for Intrusion Detection based on Data Mining Techniques", Proceedings of IEEE, 2012.
- Liang Hu, Taihui Li, Nannan Xie, Jiejun hu, "False Positive Elemination in Intrusion Detection based on Clustering", IEEE International Conference on Funny System and Knowledge Discovery (FSKD), pp. 519-523, 2015.
- Zhengjie Li, Yongzhong Li, Lei Xu, "Anomoly Intrusion Detection Method based on K-Means Clustering Algorithm with Particle Swarm Optimization", IEEE International Conference of Information Technology, Computer Engineering and Management Sciences, pp. 157- 161, 2011.
- S. J. Horng, M.Y. Su, Y. H. Chen, T. W. Kao, R. J. Chen, J. L. Lai, C. D. Perkasa, "A novel intrusion detection system based on hierarchical clustering and support vector machines", Proceedings of Science direct: Expert Systems with Applications, pp. 306-313, 2011.
- Whatistarget.com/definition/confidentiality-integrity-and-availability-CIA
- J. Han, M. Kamber, "Data Mining: Concepts and Technnologies", Third Edition.
- http://nsl.cs.unb.ca/NSL-KDD/
- Dae-Ki Kang and Doug Fuller et al., "Learning Classifiers for Misuse and Anomaly Detection Using a Bag of System Calls Representation", IEEE Workshop on Information Assurance and Security United States Military Academy (2005).
- K. Shivshankar E., "Combination of Data Mining Techniques for Intrusion Detection System", IEEE International Conference on Computer, Communication and Control (IC4-2015).
- Jain Patik P and Madhu B.R., "Data Mining based CIDS: Cloud Intrusion Detection System for Masquerade attacks [DCIDSM]", IEEE 4th ICCCNT (2013).
- J.Yang, R.Yan, A.G.Hauptman, "Cross-Domain Video Concept Detection Using Adaptive SVMs", Proceedings of ACM, MM'07, Augsburg, Bavaria, Germany, 2007.
