Using Website Content for Detecting Phishing URLs: A Novel Approach

Phishing attacks have become a rampant cybercrime, where hackers use deceptive methods to trick individuals into sharing their personal information. However, with the advent of AI models like ChatGPT, detecting phishing URLs accurately has become increasingly challenging for traditional machine learning techniques due to the creation of fake content by hackers. To address this issue, we propose a new feature called “human-content” that helps in differentiating legitimate and phishing websites based on human-generated content on a website. To classify phishing URLs, we employed various features, such as domain-based, JavaScript-based, and lexical-based features, along with the novel “human-content” feature. To perform the classification, we used five machine learning classifiers, including Gradient Boosting Classifier, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression. Our experimental results on a dataset consisting of both legitimate URLs obtained from Alexa and phishing URLs from PhishTank demonstrate that the Gradient Boosting Classifier has the best performance, achieving an accuracy of 95.8%. Our proposed approach provides promising results in detecting phishing URLs and is more robust to AI-generated fake content. As phishing attacks continue to become more sophisticated, our method can help enhance online user security by effectively detecting phishing URLs.