Intelligent phishing website detection system using fuzzy techniques

Aburrous, Maher, Hossain, Alamgir, Thabtah, Fadi and Dahal, Keshav (2008) Intelligent phishing website detection system using fuzzy techniques. In: Proceedings of the 3rd International Conference on Information and Communication Technologies: From Theory to Applications. IEEE, Piscataway, NJ, pp. 1-6. ISBN 978-1424417513

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Phishing Websites are forged Web pages that are created by malicious people to mimic Web pages of real Websites and it attempts to defraud people of their personal information. Detecting and identifying Phishing Websites is really a complex and dynamic problem involving many factors and criteria, and because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Logic model can be an effective tool in assessing and identifying phishing Websites than any other traditional tool since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the 'fuzziness' in traditional Website phishing risk assessment and propose an intelligent resilient and effective model for detecting phishing Websites. The proposed model is based on FL operators which is used to characterize the Website phishing factors and indicators as fuzzy variables and produces six measures and criteria's of Website phishing attack dimensions with a layer structure. Our experimental results showed the significance and importance of the phishing Website criteria (URL & Domain Identity) represented by layer one, and the variety influence of the phishing characteristic layers on the final phishing Website rate.

Item Type: Book Section
Additional Information: Paper presented at the 3rd International Conference on Information and Communication Technologies : from Theory to Applications : Damascus, Syria 7-11 April 2008.
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Sarah Howells
Date Deposited: 26 Oct 2012 10:40
Last Modified: 12 Oct 2019 22:29

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