Phishing Detection Using Machine Learning

Phishing attacks are one of the most common cybersecurity threats that aim to steal sensitive information such as usernames, passwords, and financial details. This research proposes a machine learning-based phishing detection system that analyses website features to classify them as phishing. The system uses URL-based and domain-based features such as URL length, HTTPS presence, and domain age. A random forest algorithm is implemented to improve detection accuracy. The results show that the proposed system achieves high accuracy and can effectively detect phishing websites, helping users stay safe online. Although many methods have been proposed to detect phishing websites, Phishers have evolved their methods to escape from these detection methods. One of the most successful methods for detecting these malicious activities is machine learning. In this paper, we compared the results of multiple machine learning methods for predicting phishing websites.