PHISHING WEBSITE DETECTION

This paper proposes a machine learning–based system for detecting phishing websites by analyzing URL, domain, and content-based features. The system aims to identify fraudulent websites that attempt to deceive users into revealing sensitive information such as login credentials and financial details. By eliminating reliance on traditional blacklist-based methods, the proposed approach enables proactive detection of newly created phishing websites. Supervised machine learning algorithms are trained to classify websites as legitimate or phishing with high accuracy. The system improves detection efficiency, reduces false positives, and supports real-time implementation for enhanced cybersecurity. The proposed solution contributes to improved online safety, transparency, and protection against cyber threats in modern digital environments.