DaE2: Unmasking malicious URLs by leveraging diverse and efficient ensemble machine learning for online security

Abstract

Over 5.44 billion people now use the Internet, making it a vital part of daily life, enabling communication, e-commerce, education, and more. However, this huge Internet connectivity also raises concerns about online privacy and security, particularly with the rise of malicious Uniform Resource Locators (URLs). Recently, conventional ensemble models have attracted attention due to their notable benefits of reducing the variance in models, enhancing predictive performance, improving prediction accuracy, and demonstrating high generalization potential. But, its application in addressing the challenge of malicious URLs is still an open problem. These URLs often hide behind static links in emails or web pages, posing a threat to individuals and organizations. Despite blacklisting services, many harmful sites evade detection due to inadequate scrutiny or recent creation. Hence, to improve URL detection, a Diverse and Efficient Ensemble (DaE2) machine learning algorithm was developed using four ensemble models, that is, AdaBoost, Bagging, Stacking, and Voting to classify URLs. After preprocessing, the experimental result shown that all models achieved over 80 % accuracy, with AdaBoost reaching 98.5 % and Stacking offering the fastest runtime. AdaBoost and Bagging also delivered strong performance, with F1 scores of 0.980 and 0.976, respectively.