| dc.description.abstract | In recent years, phishing websites have become a significant threat in the realm of cybersecurity, posing severe risks to the data security of individuals and organizations. Phishing websites are those that masquerade as legitimate sites, attempting to obtain sensitive information from users, such as account passwords and credit card numbers, for illicit activities. These sites often employ deceptive tactics, such as fake login pages and misleading emails, to trick users into clicking and interacting, thereby stealing information or installing malware.
To address this issue, this system proposes a novel phishing website detection method that combines the analysis of pre-login and post-login screens to enhance the accuracy of phishing site detection. If it is not possible to identify a phishing site through the analysis of pre-login and post-login screens, the system will filter the source code before and after login, retaining the necessary code as input for an AI method, which will then determine whether the site is a phishing website. The objective of this research is to develop a comprehensive and effective phishing website detection system that helps users identify and prevent phishing attacks, thereby protecting the data security of individuals and organizations. | en_US |