Phishing Attack Detection on Websites Using Machine Learning
Leonika S.V., VR. Nagarajan · Journal of Soft Computing Paradigm · 2025
Phishing attacks usually copy reliable websites, such as banks and financial institutions, in an attempt to obtain personal information, such as passwords and credit card details. This study suggests a hybrid phishing detection system that combines the Back Propagation Neural Network (BPNN) for classification with XGBoost for feature selection. For training (80%) and testing (20%), a dataset of 11,000 URLs was employed, including both phishing and authentic samples. Important URL-based characteristics were extracted, including URL length, discrepancy character, HTTPS appearance, and domain age. High identification accuracy (97.5%), precision (96.8%), recall (98.2%), and F1-score (97.5%) were obtained by all systems. When compared to traditional classifiers (SVM, Random Forest), the proposed model shows better performance in identifying zero-day phishing efforts, Explanation measures were used to assess the model, and Scikit-LARN was used to simulate it in python. The results confirm that the algorithm can successfully detect and stop phishing efforts in real time.