A Novel Mechanism for Identifying Malicious and Phishing URLs Using Machine Learning Classifiers

Phishing attacks are a straightforward method for obtaining sensitive information from unsuspecting users. The goal of phishers is to acquire critical data such as usernames, passwords, and bank account details. The threat of network information insecurity is rapidly increasing in both frequency and severity. Domain phishing involves deceiving email recipients into revealing their account details through links in emails masquerading as legitimate registrars. A phishing URL is designed to promote scams, attacks, and fraud. When clicked, these URLs can lead to ransomware downloads, phishing attempts, or other cybercrimes. Hackers commonly exploit end-to-end technology and human vulnerabilities using techniques like social engineering, phishing, and pharming. Cybersecurity professionals are now seeking reliable and robust methods for detecting phishing websites. This project utilizes machine learning to detect phishing URLs by analyzing various features of legitimate and phishing URLs. The algorithms employed include Decision Tree, Logistic Regression, Gradient Boosting, and Support Vector Machine. The objective of this project is to identify phishing URLs and determine the most effective machine learning algorithm by comparing accuracy rates, false positives, and false negatives of each algorithm.