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arXiv · 2505.21141

A Predicting Phishing Websites Using Support Vector Machine and MultiClass Classification Based on Association Rule Techniques

Nancy C. Woods·Virtue Ene Agada·Adebola K. Ojo
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Abstract

Phishing is a semantic attack which targets the user rather than the computer. It is a new Internet crime in comparison with other forms such as virus and hacking. Considering the damage phishing websites has caused to various economies by collapsing organizations, stealing information and financial diversion, various researchers have embarked on different ways of detecting phishing websites but there has been no agreement about the best algorithm to be used for prediction. This study is interested in integrating the strengths of two algorithms, Support Vector Machines (SVM) and Multi-Class Classification Rules based on Association Rules (MCAR) to establish a strong and better means of predicting phishing websites. A total of 11,056 websites were used from both PhishTank and yahoo directory to verify the effectiveness of this approach. Feature extraction and rules generation were done by the MCAR technique; classification and prediction were done by SVM technique. The result showed that the technique achieved 98.30% classification accuracy with a computation time of 2205.33s with minimum error rate. It showed a total of 98% Area under the Curve (AUC) which showed the proportion of accuracy in classifying phishing websites. The model showed 82.84% variance in the prediction of phishing websites based on the coefficient of determination. The use of two techniques together in detecting phishing websites produced a more accurate result as it combined the strength of both techniques respectively. This research work centralized on this advantage by building a hybrid of two techniques to help produce a more accurate result.

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BibTeXRIS

Nancy C. Woods, Virtue Ene Agada, Adebola K. Ojo. 2025-05-27. A Predicting Phishing Websites Using Support Vector Machine and MultiClass Classification Based on Association Rule Techniques. https://arxiv.org/abs/2505.21141

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