Intelligent phishing website detection: A CNN-SVM approach with nature-inspired hyperparameter tuning

Phishing attacks represent a growing threat to online users and software developers, necessitating the development of advanced detection strategies. This study proposes a hybrid framework that integrates convolutional neural networks (CNN) for feature extraction and support vector machines (SVM) for classification, with the SVM optimized using the grey wolf optimizer (GWO). The CNN component is responsible for capturing complex and discriminative patterns from website data, enabling more effective differentiation between phishing and legitimate websites. Hyperparameter tuning via GWO enhances the classification performance of the SVM by generating an optimal decision boundary. Evaluation was conducted using established datasets, including those from Kaggle, the UCI Machine Learning Repository, Phishtank, 5000 Best Websites, and Alexa. Experimental results show that the CNN–SVM model, with GWO optimization, achieved an accuracy of 99.18 %, indicating its practical utility in phishing detection applications. The findings suggest that the proposed framework, supported by additional security mechanisms, contributes to a reduction in false positives while maintaining reliable detection of phishing threats.