PHISHING WEBSITE DETECTION USING MACHINE LEARNING

The Phishing Website Detection Using Machine Learning project offers a robust and intelligent solution to counter the growing threat of phishing attacks, which continue to compromise online security worldwide. The system focuses on extracting and analyzing multiple URL-based features that help differentiate between legitimate and malicious websites. Key indicators such as domain age, URL length, presence of an IP address, HTTPS usage, redirection patterns, prefix-suffix in domains, URL depth, and the detection of URL shortening services are systematically evaluated. These features are extracted through methods including whois lookups, regular expression matching, and HTML/JavaScript content analysis, enabling the system to capture subtle anomalies often used by phishing sites to deceive users. For example, unusually long URLs, the presence of “@” or “-” symbols, or excessive redirections are flagged as suspicious attributes.

Keywords:

Phishing Detection, Machine Learning, URL Feature Extraction, Random Forest Classifier, Cybersecurity, Web Application Security, Malicious Website Classification.