Detecting Phishing Websites Using Natural Language Processing
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Abstract
Phishing is one of the most cyber attacking tools. It targets both users and organizations. Several solutions have been proposed for detecting and preventing phishing websites, emails and SMSs. However, more research works are required to improve the phishing detection techniques such as improving the detection scalability and reducing false positive and negative alerts. This paper proposes a website phishing detection system based on natural language processing (NLP) features such as statements, words, and characters frequency. The proposed system first enables any user to find out if a specific website is phishing or not and, second, provides a search engine that 24/7 searches for the phishing websites and informs the system administrator (or publishes alerts online) about that. The system is evaluated in terms of its scalability and accuracy. The system accuracy here relies on the number of false-positive, false negative, true positive, and true negative alerts.
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