Identification of Phishing URL Using Stacking Ensemble Machine Learning Technique

The occurrence of phishing attacks targeting internet users are increasing nowadays. Cybercriminals are using fake websites to fake individuals to reveal their personal information. This study aims to enhance online security by applying latest ml and deep learning methods to identify phishing URLs to increase accuracy and reliability of its predictions. The deep learning-based feature extraction techniques are paired with ensemble techniques including Catboost, lightGBM, Extreme gradient boost, and random forest in this model. The experiment's outcome demonstrates a notable increase in the effectiveness and accuracy of current predictions. This study creates a base for the creation of real-time phishing detection systems and illustrates the effectiveness of advanced techniques in combating phishing threats.