Phishing attacks remain a pervasive threat in the cybersecurity landscape, necessitating intelligent and scalable detection mechanisms. This paper suggests a deep learning-based method for phishing URL identification using Convolutional Neural Networks (CNNs) on two benchmark datasets: the Phishing and PhishTank datasets. The CNN model eliminates the need for human feature engineering by automatically learning intricate, non-linear patterns from structured information. The Phishing dataset undergoes 5-fold cross-validation to guarantee robustness, and the results are contrasted with those of conventional classifiers like XGBoost and Logistic Regression. According to the results, the CNN routinely beats these baselines in terms of accuracy and F1-score. Notably, on the PhishTank dataset, the CNN achieves exceptional performance with over 99.3% accuracy, underscoring its effectiveness and generalizability. The experimental framework is implemented using TensorFlow in Python and validated on a standard computing setup. The findings reinforce CNN’s suitability for real-time, adaptive phishing detection in dynamic threat environments.
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