Data Mining-Driven Detection of Banking URL Phishing Using Hybrid CNN and Machine Learning Models

Phishing attacks remain one of the primary cybersecurity threats because attackers exploit users through deceptive URLs focusing primarily on online banking. The research undertakes a comparative assessment between deep learning and machine learning detection algorithms applied to find phishing URLs focused on banking clients. The research implemented Convolution Neural Network (CNN), Long Short-Term Memory (LSTM) and alongside Logistic Regression (LR) and Random Forest (RF) and Support Vector Machine (SVM) and XGBoost as independent training models for evaluation. The CNN model proved its superiority in identifying complicated patterns of malicious URLs because it reached 99% accuracy during testing and training. Public performance standards enabled the assessment of robust model behavior through an extensive evaluation process. Upcoming research will investigate instant threat identification techniques as well as extra feature integration strategies to boost system performance for protective measures in changing security threats.