Phishing URL Detector: An Ensemble Approach using Machine Learning Models

The growing ubiquity of Internet use has significantly enhanced communication and data exchange. However, it has also Increased exposure to cyber threats such as phishing, where attackers attempt to deceive users into revealing sensitive information by mimicking legitimate websites. This research presents a machine learning-based approach to detecting phishing URLs. Using a data set comprising more than 11,000 website records, we extracted features that distinguish phishing sites from legitimate ones. The study explores various machine learning algorithms, evaluates their performance, and examines the efficiency in identifying phishing threats. Our results suggest that ML significantly strengthens the use of machine learning in enhancing cybersecurity measures.