(Faculty of Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(2) Iwan Tri Riyadi Yanto
(Faculty of Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(3) Azizul Azhar Ramli
(Faculty of Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(4) Mohd Farhan Md. Fudzee
(Faculty of Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)*corresponding author
AbstractPhishing is a serious web security problem, and the internet fraud technique involves mirroring genuine websites to trick online users into stealing their sensitive information and taking out their personal information, such as bank account information, usernames, credit card, and passwords. Early detection can prevent phishing behavior makes quick protection of personal information. Classification methods can be used to predict this phishing behavior. This paper presents an intelligent classification model for detecting Phishing by redefining a fuzzy soft set (FSS) theory for better computational performance. There are four types of similarity measures: (1) Comparison table, (2) Matching function, (3) Similarity measure, and (4) Distance measure. The experiment showed that the Similarity measure has better performance than the others in accuracy and recall, reached 95.45 % and 99.77 %, respectively. It concludes that FSS similarity measured is more precise than others, and FSS could be a promising approach to avoid phishing activities. This novel method can be implemented in social media software to warn the users as an early warning system. This model can be used for personal or commercial purposes on social media applications to protect sensitive data.
KeywordsSimilarity measure; Fuzzy soft set; Phising detection; Classification
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DOIhttps://doi.org/10.26555/ijain.v7i1.605 |
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