Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems

Fehringer, Gerhard and Barraclough, Phoebe (2017) Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems. International Journal of Advanced Computer Science and Applications, 8 (6). pp. 1-10. ISSN 2156-5570

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Anti-phishing detection solutions employed in industry use blacklist-based approaches to achieve low false-positive rates, but blacklist approaches utilizes website URLs only. This study analyses and combines phishing emails and phishing web-forms in a single framework, which allows feature extraction and feature model construction. The outcome should classify between phishing, suspicious, legitimate and detect emerging phishing attacks accurately. The intelligent phishing security for online approach is based on machine learning techniques, using Adaptive Neuro-Fuzzy Inference System and a combination sources from which features are extracted. An experiment was performed using two-fold cross validation method to measure the system’s accuracy. The intelligent phishing security approach achieved a higher accuracy. The finding indicates that the feature model from combined sources can detect phishing websites with a higher accuracy. This paper contributes to phishing field a combined feature which sources in a single framework. The implication is that phishing attacks evolve rapidly; therefore, regular updates and being ahead of phishing strategy is the way forward.

Item Type: Article
Uncontrolled Keywords: phishing websites, fuzzy model, intelligent detection
Subjects: G400 Computer Science
G500 Information Systems
G600 Software Engineering
G700 Artificial Intelligence
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Phoebe Barraclough
Date Deposited: 09 May 2018 11:47
Last Modified: 01 Aug 2021 08:53

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