Abstract
Intrusion detection is one of the major research problems in network security. It is the process of monitoring and analyzing the events occurring in a computer system in order to detect different security violations. Mining approach can play a very important role in developing an intrusion detection system. In this paper, we present the comparison of different classification techniques to detect and classify intrusions into normal and abnormal behaviors. The algorithms used are J48, Naive Bayes, JRip, and OneR. We use the WEKA tool to evaluate these algorithms. The experiments and assessments of these methods are performed with NSL-KDD intrusion detection dataset. Our main aim was to show the comparison of the different classification algorithms and find out which algorithm will be most suitable for the intrusion detection. We also summarize the research challenges in classification process.
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Chauhan, H., Kumar, V., Pundir, S., Pilli, E.S. (2014). Comparative Analysis and Research Issues in Classification Techniques for Intrusion Detection. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_68
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DOI: https://doi.org/10.1007/978-81-322-1665-0_68
Publisher Name: Springer, New Delhi
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