Data Mining based Hybrid Intrusion Detection System

Chandrashekhar Azad  and Vijay Kumar Jha

doi:10.17485/ijst/2014/v7i6.19

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Data Mining based Hybrid Intrusion Detection System

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2014/v7i6.19

Year: 2014, Volume: 7, Issue: 6, Pages: 781–789

Original Article

Data Mining based Hybrid Intrusion Detection System

Chandrashekhar Azad* and Vijay Kumar Jha

Dept. of Information Technology, Birla Institute of Technology, Mesra, Ranchi, Jhrakhand, India; csazad@bitmesra.ac.in, vkjha@bitmesra.ac.in

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

An intrusion detection system is proposed using Decision Table/Naïve Bayes (DTNB). The Proposed system uses a hybrid classifier DTNB that is used to identify possible intrusions. The system is trained using a subset of the NSL KDD Cup dataset. The trained model is then tested using a subset of NSL KDD Cup dataset. The DTNB hybrid classifier is able to detect intrusion with a superior detection rate.

Keywords: Anomaly Detection, DTNB, IDS, Misuse Detection