Abstract
In recent years, Web-based attacks have become one of the most common threats. Threat actors tend to use malicious URLs to intentionally deceive users and launch attacks. Several approaches such as blacklisting have been implemented to detect malicious URLs. These unreliable approaches were also accompanied with strenuous task of maintaining an up-to-date blacklist URL database. To detect malicious URLs, machine learning techniques have been explored in recent years. This method analyzes different features of a URL and trains a prediction model on an already existing dataset of both malicious and benign URLs. This paper proposes a MuD (Malicious URL Detection) model which utilizes three supervised machine learning classifiers—support vector machine, logistic regression and Naive Bayes—to effectively and accurately detect malicious URLs. The preliminary results indicate that Naive Bayes algorithm produced best results.
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