TY  - EJOU
AU  - Alshinwan, Mohammad 
AU  - Alayed, Walaa 
AU  - Hashim, Fatma A. 
AU  - Tawil, Arar Al 

TI  - A Novel Metaheuristic Approach for Phishing Websites Detection with the Modified Differential Evolution Algorithm
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 89
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Websites phishing; meta-heuristics optimization algorithms; differential evolution; random forest; gradient boosting

DO  - 10.32604/cmc.2026.086257