Transformer-based Phishing Website Detection and Cyber Attack Prevention Systems

Abstract

Phishing websites still pose a threat to internet users by using well-known domain names and confusing URL formats to trick them into divulging confidential information. This research proposes a phishing website detection system and a cyberattack prevention system based on a deep learning model designed to detect whether raw URLs are phishing or legitimate, leveraging a transformer model. The system analyzes URL sequences to identify patterns, domain name irregularities, suspicious tokens, unusual lengths, special character usage, and deceptive subdomains. A web interface enables the user to input URLs, and the Flask backend performs pre-processing, tokenization, model inference, and suspicious feature identification. The proposed method fuses the transformer-based semantic representation of the URL and lexical feature analysis to reliably detect phishing and provide explainable warning features. The system creates a prediction label, confidence score, and suspicious features in real time to support the decision-making process. This work provides a practical and scalable solution for phishing identification, user protection, and web-based cyberattack prevention.