Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System

Albayati, Mohanad and Issac, Biju (2015) Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System. International Journal of Computational Intelligence Systems, 8 (5). pp. 841-853. ISSN 1875-6891

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Official URL: http://dx.doi.org/10.1080/18756891.2015.1084705

Abstract

In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we developed a Java software with three most efficient classifiers and compared it with other options. The outputs would show the detection accuracy and efficiency of the single and combined classifiers used.

Item Type: Article
Uncontrolled Keywords: Intrusion Detection, Data Mining, Machine Learning, Detection accuracy
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 12 Oct 2018 08:05
Last Modified: 11 Oct 2019 17:52
URI: http://nrl.northumbria.ac.uk/id/eprint/36295

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