Cutting-Edge Phishing Detection Using Novel Features and Hybrid Machine Learning Techniques

Classification Information Mining (DM) strategies are highly effective tools for identifying and detecting e-banking phishing websites. This study introduces a novel approach to address the challenges and complexities associated with predicting and identifying such websites. It leverages advanced association and classification information mining techniques to develop a robust and efficient detection method. The proposed calculations identified key components and rules required for classifying phishing websites and understanding the relationships among them. A phishing case was connected to demonstrate the site's phishing preparation. The rules created from the affiliated classification show the relationship between a few critical characteristics like URL and Space Character, as well as security and encryption criteria, in the ultimate phishing location rate. The test illustrates the possibility of utilizing Acquainted Classification procedures in genuine applications and its superior execution compared to other conventional classification calculations. To predict the phishing websites, this study employed the Light Gradient Boosting Classification (LGBC) model. Additionally, three novel metaheuristic algorithms, the Prairie Dog Optimization Algorithm (PDOA), Reptile Search Algorithm (RSA), and Logic Optimization Algorithm (LOA) were utilized to enhance the performance of the LGBC model. According to the trial data, the LGLO hybrid model outperformed other algorithms. In the accuracy value at the train section, the LGLO model with the value of (0.911) has the best performance compared to the LGRS and LGPD models.