Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Abstract: Security concerns in internet communication, particularly phishing attacks, pose significant challenges to safeguarding user information. Phishing attacks aim to steal personal data by mimicking legitimate websites, making traditional detection methods less effective. Machine learning-based approaches, though widely used, often struggle with concealed phishing websites and evolving tactics by attackers. Key advancements include the integration of more layers and larger training datasets, along with feature extraction from the Phish Tank dataset. Our proposed model comprises seven layers, culminating in a specialized output layer structure. Experiment results showcase the efficacy of our approach, underscoring its potential to significantly enhance internet security. Furthermore, our model's ability to adapt to highly concealed phishing websites and the dynamic nature of attackers' tactics marks a significant improvement over existing methodologies. By incorporating a comprehensive feature set and leveraging deep learning techniques, our approach achieves state-of-the-art performance in phishing website detection.