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Erschienen in:
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2014 | OriginalPaper | Buchkapitel

Comparative Analysis and Research Issues in Classification Techniques for Intrusion Detection

verfasst von : Himadri Chauhan, Vipin Kumar, Sumit Pundir, Emmanuel S. Pilli

Erschienen in: Intelligent Computing, Networking, and Informatics

Verlag: Springer India

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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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Vorheriges Kapitel An Appraisal of Service-Based Virtual Networks and Virtualization Tools Paves the Way Toward Future Internet
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Metadaten
Titel
Comparative Analysis and Research Issues in Classification Techniques for Intrusion Detection
verfasst von
Himadri Chauhan
Vipin Kumar
Sumit Pundir
Emmanuel S. Pilli
Copyright-Jahr
2014
Verlag
Springer India
Buch
Intelligent Computing, Networking, and Informatics

Print ISBN: 978-81-322-1664-3

Electronic ISBN: 978-81-322-1665-0

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-81-322-1665-0_68