Detecting Targeted Phishing Websites for Brand Protection and Cyber Defence Using Computer Vision

Cybercrime continues to pose a significant threat to modern society, requiring a solid emphasis on cyber-attack prevention, detection and response by civilian and military organisations aimed at brand protection. This study applies a novel framework to identify, detect and mitigate phishing attacks, leveraging the power of computer vision technology and artificial intelligence. The primary objective is to automate the classification process, reducing the dwell time between detection and executing courses of action to respond to phishing attacks. When applied to a real-world curated dataset, the proposed classifier achieved relevant results with an F1-Score of 95.76% and an MCC value of 91.57%. These metrics highlight the classifier’s effectiveness in identifying phishing domains with minimal false classifications, affirming its suitability for the intended purpose. Future enhancements include considering a fuzzy logic model that accounts for the classification probability in conjunction with the domain creation date and the uniqueness of downloaded resources when accessing the website or domain.