Addressing Phishing Threats Using a Metaheuristic Perspective on Machine Learning Classification Models Code

Bo  Hu; SaiNan  Zhang

doi:10.6180/jase.202507_28(7).0011

Addressing Phishing Threats Using a Metaheuristic Perspective on Machine Learning Classification Models Code

Bo HuThis email address is being protected from spambots. You need JavaScript enabled to view it. and SaiNan Zhang

Center of information construction and management, Nanjing Normal University of Special Education, Nanjing 210038, China

Received: April 24, 2024
Accepted: September 1, 2024
Publication Date: October 7, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202507_28(7).0011

Web phishing attacks have emerged as a significant threat to online security, enabling phishers to steal sensitive financial information and commit fraud. To combat this, many anti-phishing systems have been developed, focusing on detecting phishing content in online communications. This study introduces novel approaches to enhance phishing detection by employing machine learning techniques. Specifically, three different single models were analyzed: Random Forest Classifier (RFC), Adaptive Boosting Classification (ADAC), and Naïve Bayes Classification Algorithm (NBC). These models were optimized using Artificial Rabbits Optimization (ARO), resulting in hybrid models RFAR, NBAR, and ADAR. The results of the models’ analysis indicate that the RFAR hybrid model performs better than the other single models and their optimized models. The RFAR model achieved precision scores of 0.950 for phishing websites, 0.954 for suspicious websites, and 0.872 for legitimate websites, with corresponding recall values of 0.929, 0.954, and 0.990 , respectively. In comparison, the ADAR model was notably effective in classifying legitimate websites with a precision score of 0.896 . The study’s novelty lies in integrating ARO with traditional classifiers to create hybrid models that improve classification accuracy.

Keywords: Phishing; Cyber Attacks; Classification; Data Mining; Optimization Algorithms; Phishing Websites Prediction; Artificial Intelligence

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