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Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification


Citation

Juma, Sundus and Muda, Zaiton and Yassin, Warusia (2014) Reducing false alarm using hybrid Intrusion Detection based on X-Means clustering and Random Forest classification. Journal of Theoretical and Applied Information Technology, 68 (2). pp. 249-254. ISSN 1992-8645; ESSN: 1817-3195

Abstract

In recent times, Intrusion Detection systems (IDSs) incarnate the high network security. Anomaly-based intrusion detection techniques, that utilize algorithms of machine learning, have the capability to recognize unpredicted malicious. Unluckily, an essential provocation of this method is to maximize accuracy, detection whereas minimize false alarm rate. This paper proposed a hybrid machine learning approach based on X-Means clustering and Random Forest classification called XM-RF in order to aforementioned drawbacks. X-Means clustering is utilized to gather whole data into congruent cluster based on their behaviour whereas Random Forest classifier is utilized to rearrange the misclassified clustered data to apropos group. The ISCX 2012 Intrusion Detection Evaluation is used as a model dataset. The experimental result pose that the proposed approach obtains better than other techniques, with the accuracy, detection and false alarm rates of 99.96%, 99.99%, and 0.2%, respectively.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
Publisher: Asian Research Publishing Network (A R P N)
Keywords: Intrusion Detection system; Anomaly-based intrusion detection; Machine learning, X-Means; Random Forest
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 31 Dec 2015 02:12
Last Modified: 31 Dec 2015 02:12
URI: http://psasir.upm.edu.my/id/eprint/35184
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