Open Access

ARTICLE

A Novel Metaheuristic Approach for Phishing Websites Detection with the Modified Differential Evolution Algorithm

Mohammad Alshinwan^1,*, Walaa Alayed^2,*, Fatma A. Hashim³, Arar Al Tawil⁴

1 Department of Computer Science, Faculty of Information Technology, Al al-Bayt University, Mafraq, Jordan
2 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
3 Biomedical Engineering Department, Faculty of Engineering, Capital University (Formerly Helwan University), Cairo, Egypt
4 Computer Sciences Department, Faculty of Information Technology, Applied Science Private University, Amman, Jordan

* Corresponding Authors: Mohammad Alshinwan. Email: ; Walaa Alayed. Email:

Computers, Materials & Continua 2026, 89(2), 84 https://doi.org/10.32604/cmc.2026.086257

Received 27 May 2026; Accepted 06 August 2026; Issue published 15 September 2026

Abstract

The increasing trend of phishing sites is among the important threats against the Internet security, associated with monetary loss, data leakage, and identity swindle. In response to this urgent problem, this paper proposes a new phishing website detection framework based on the Modified Differential Evolution (mDE) algorithm in conjunction with state-of-the-art machine learning classifiers. The proposed mDE integrates with dynamic mutation and crossover strategies to improve the global search capability and the convergence speed, which is superior to traditional single optimization methods. We conduct experiments on two benchmark datasets: the UCI Phishing Websites dataset and the large-scale Mendeley Phishing URLs dataset. The proposed optimizer is implemented as an improved variant, mDE+, which augments the adaptive control parameters with opposition-based initialization, guided mutation, and self-adaptive control of the mutation and crossover factors. Using Random Forest and gradient-boosting classifiers, mDE+ selects compact feature subsets that are competitive with, and on most settings superior to, those produced by standard DE and other nature-inspired optimizers (GWO and COA), with statistically significant gains over DE and COA on UCI and negligible additional computational overhead. Experimental results demonstrate that the proposed approach outperforms the state-of-art methods in terms of accuracy, precision, recall and F1-score, which further verify the effectiveness and robustness of the proposed method. This paper shows that mDE is a promising optimization method for training phishing detection systems for real-world cyber security applications.

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Keywords

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