Comparative analysis of machine learning algorithms for phishing website detection

Kumaraswamy S 1, Saishravan Nitish Nayak 2, *, Vinodh Kumar N 2 and Mohammad Waseem 2

1 Department of Computer Science and Engineering, University Visveswaraya College of Engineering, India.
2 University Visveswaraya College of Engineering, India.
 
Review
International Journal of Science and Research Archive, 2024, 12(01), 293–298.
Article DOI: 10.30574/ijsra.2024.12.1.0796
 
Publication history: 
Received on 25 March 2024; revised on 01 May 2024; accepted on 04 May 2024
 
Abstract: 
As phishing assaults continue to pose a serious hazard in the digital world, trustworthy detection techniques are required. The effectiveness of machine learning techniques in detecting phishing websites is investigated in this study. The best-performing models were XGBoost and Multilayer Perceptrons (MLPs), which obtained test data accuracy of 90.4% and 90.3%, respectively. On the test data, the Random Forest and Decision Tree models showed competitive accuracies of 86.5% and 87.3%, respectively. SVMs, or support vector machines, performed admirably as well, obtaining an accuracy of 86.4% on the test set. Notably, with accuracy of 74.0% on the test data, the Autoencoder Neural Network demonstrated a restricted level of efficacy. These results highlight the effectiveness of XGBoost and MLPs in precisely detecting phishing websites, offering academics and practitioners in cybersecurity useful information.
 
Keywords: 
Phishing Websites; Machine Learning; Decision Trees; Random Forests; XGBoost; Support Vector Machines; Autoencoder Neural Network; Multilayer Perceptrons
 
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