Assembly Classifier Approach to Analyze Intrusion Detection Dataset in Networks by Using Data Mining Techniques
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

Research Paper | Computer Science & Engineering | Sudan | Volume 4 Issue 4, April 2015

Assembly Classifier Approach to Analyze Intrusion Detection Dataset in Networks by Using Data Mining Techniques

Ayad Mohammed Mahyoub Ghaleb, Samani A. Talab

The Intrusion Detection system analyzes network traffic to detect the attacks. The attack detection methods used by these systems are of two types anomaly detection and misuse detection methods. Intrusion detection is a type of security management system for computer networks. An Intrusion detection system analyzes information within a computer network to identify possible security breaches, which include both anomaly and misuse. In this paper I studied the performance of number of data mining algorithms and chose best three algorithms for building multi classifier from decision tree classifier, nave Bayes classifier and Multilayer Perceptron classifier. I evaluated performance classifier by account accuracy and error rate.

Keywords: Intrusion Detection, Intrusion Detection System IDS, Probe attacks, Dos Denial of Service attacks, R2L Remote to Local attack, U2R User to Root attack, Weka, NLS-KDD 99 data set

Edition: Volume 4 Issue 4, April 2015

Pages: 742 - 748

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How to Cite this Article?

Ayad Mohammed Mahyoub Ghaleb, Samani A. Talab, "Assembly Classifier Approach to Analyze Intrusion Detection Dataset in Networks by Using Data Mining Techniques", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SUB153071, Volume 4 Issue 4, April 2015, 742 - 748

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