Abstract
The security of computer networks has been in the focus of research for years. Organizations have realized that network security technology has become very important in protecting its information. Any attempt, successful or unsuccessful to compromise the confidentiality, integrity and availability of any information resource or the information itself is considered as a security threat or an intrusion. Every day, new kinds of threats are being faced by industries. One of the way-out to this problem is by using Intrusion Detection System (IDS). The main function of IDS is distinguishing and predicting normal or abnormal behaviors. This paper presents new implementation strategy performing the intrusion detection system, which gives better results by improving accuracy of classification. This approach is based on by defining addition and deletion rule and updating policy for intrusion detection. The experimental results, conducted on the KDD99 dataset, prove that, this new approach outperforms several state-of-the-art methods, particularly in detecting rare attack types.
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References
Chebrolu, S., Abraham, A., Thomas, J.P.: Feature deduction and ensemble design of intrusion detection systems. Computer Security 24(4), 295–307 (2005)
Lee, W., Stolfo, S.J.: A framework for constructing features and models for intrusion detection systems. ACM Trans. Inf. Syst. Security 3(4), 227–261 (2000)
Denning, D.: An Intrusion-Detection Model. IEEE Transactions on Software Engineering SE-13(2) (February 1987)
Wu, S.X., Banzhaf, W.: The Use of Computational Intelligence in Intrusion Detection Systems: A Review. Applied Soft Computing Journal (June 2009)
Bishop, C.M.: Neural networks for pattern recognition. Oxford University, England (1995)
Manocha, S., Girolami, M.A.: An empirical analysis of the probabilistic K nearest neighbour classifier. Pattern Recognition Letters 28, 1818–1824 (2007)
Mitchell, T.: Machine learning. McGraw Hill, New York (1997)
Li, Y., Guo, L.: An active learning based TCM-K-NNalgorithm for supervised network intrusion detection. Computers & Security 26, 459–467 (2007)
Tang, H., Cao, Z.: Machine Learning Based Intrusion Detection Algorithms. Journal of Computational Information Systems 5(6), 1825–1831 (2009)
Kuang, L(V.): DNIDS: A Dependable Network Intrusion Detection System Using the CSI-K-NN Algorithm. Queen’s University Kingston, Ontario (2007)
Tsai, C.F., Hsu, Y.E., Lin, C.Y., Lin, W.Y.: Intrusion detection by machine learning: a review. Expert Systems with Applications. An International Journal 36(10) (December 2009)
KDD Cup 1999 (October 2007), http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Sarvari, H., Keikha, M.M.: Improving the Accuracy of Intrusion Detection System by Using the combination of Machine Learning Approaches. In: 2010 International Conference of Soft Computing and Pattern Recognition (2010)
Trung, N.Q.: Intrusion Detection System for Classifying Process Behavior. Thesis Stockholm, Sweden (2010)
Pathak, P., Dongre, S.: Intrusion Detection through Ensemble Classification Approach. In: NCICT 2011 (2011)
The 1998 Intrusion detection off-line evaluation plan. MIT Lincoln Lab., Information Systems Technology Group (March 25, 1998), http://www.11.mit.edu/IST/ideval/docs/1998/id98-eval-11.txt
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Wagh, S., Neelwarna, G., Kolhe, S. (2012). A Comprehensive Analysis and Study in Intrusion Detection System Using k-NN Algorithm. In: Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science(), vol 7694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35455-7_14
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DOI: https://doi.org/10.1007/978-3-642-35455-7_14
Publisher Name: Springer, Berlin, Heidelberg
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