Growing phishing attacks represent a serious challenge to the security of the computing environment-the large number of spurious websites motivated by deception and to extract sensitive information from individuals. This project proposes to reduce by exploring the transition from machine learning approaches to deep learning approaches for fake website detection.
Deep learning models, by their very nature, can automatically extract features and, therefore, are capable of identifying complex patterns and nuanced relationships within the data, which would otherwise be missed by traditional techniques. Based on methodologies like CNNs and RNNs etc, the proposed system efficiently captures complicated, deep features from the URLs and web content, therefore augmenting its ability to detect sophisticated phishing techniques. Enhancement of both the precision in detection and model adaptability to the evolving phishing tactics are considered as priorities. A comprehensive assessment shows that deep learning significantly outperforms traditional approaches, thereby facilitating a more meaningful sense of improvement in security measures in the fight against phishing attacks