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

Mohanad Albayati, Biju Issac

Research output: Contribution to journalArticleResearchpeer-review

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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.
Original languageEnglish
Pages (from-to)841-853
JournalInternational Journal of Computational Intelligence Systems
Volume8
Issue number5
DOIs
Publication statusPublished - 21 Sep 2015

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Intrusion detection
Intrusion Detection
Classifiers
Classifier
Attack
Software
Military
Java
Artificial intelligence
Artificial Intelligence
Output
Experiment
Experiments

Cite this

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Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System. / Albayati, Mohanad; Issac, Biju.

In: International Journal of Computational Intelligence Systems, Vol. 8, No. 5, 21.09.2015, p. 841-853.

Research output: Contribution to journalArticleResearchpeer-review

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