PHISHING WEBSITE DETECTION USING LSTM NETWORKS: A DEEP LEARNING APPROACH

Traditional phishing detection methods struggle to keep pace with evolving cyber threats. An LSTM-based deep learning system in order to identify fraudulent websites are presented in this study. The model effectively captures temporal dependencies and contextual patterns within webpage content, distinguishing phishing websites from legitimate ones. Additionally, an attention mechanism enhances feature extraction, improving detection accuracy. In comparison to more traditional methods, our results show significant improvements in accuracy, precision, and recall across a variety of datasets. The model's adaptability to emerging phishing strategies through continuous learning makes it a robust solution for phishing detection.