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Prediction of Phishing Websites Using Stacked Ensemble Method and Hybrid Features Selection Method

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Abstract

Phishing is considered a big concern in this age of data and digital technologies because of its significant influence on the banking and online retailing industries. Cybercriminals target all economic activity on the Internet; thus, it is critical to take security precautions to safeguard assets. One of the first steps in constructing a safe cyberspace is to prevent phishing attacks before they happen. The detection mechanisms for these assaults were created using machine learning and other methods. However, there is still room for improvement in terms of detection accuracy. This paper proposes the optimization of an ensemble classification algorithm for phishing website (PW) detection. The suggested technique was optimised using a hybrid features selection method (Chi-square, extra tree, and heatmap) by modifying numerous machine learning (ML) method parameters, including random forest, naive Bayes, J48, and KNN. These were achieved by rating the optimal classifiers and selecting the top classifiers to serve as the foundation for the suggested technique. The obtained results by all experiments show that assigned optimized stacking ensemble approach outperforms previous ML-based detection methods. The level of precision attained was 99.7%.

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Authors and Affiliations

  1. Department of Computer Applications, VBS Purvanchal University, Jaunpur, Uttar Pradesh, 222001, India

    Mithilesh Kumar Pandey, Munindra Kumar Singh & Saurabh Pal

  2. Department of Electronics and Communication, VBS Purvanchal University, Jaunpur, Jaunpur, Uttar Pradesh, 222001, India

    B. B. Tiwari

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  1. Mithilesh Kumar Pandey
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  2. Munindra Kumar Singh
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Correspondence to Saurabh Pal.

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M Shivakumar.

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Pandey, M.K., Singh, M.K., Pal, S. et al. Prediction of Phishing Websites Using Stacked Ensemble Method and Hybrid Features Selection Method. SN COMPUT. SCI. 3, 488 (2022). https://doi.org/10.1007/s42979-022-01387-4

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