Super Learner for Malicious URL Detection

Abstract

Malicious Uniform Resource Locator (URL) detection is one of the prominent research areas in Cyber security. Machine learning and statistical models are mainly used for this task due to their ability to adapt complex patterns. This research study mainly focused on implementing a machine learning classifier model using Super Learner ensemble to classify malicious URLs. Static feature set is extracted using only the URL information with less latency and reduced computational complexity to support offline and real-time detection. Proposed binary classifier model is used to separate malicious URLs from benign ones whereas the proposed multi-class classifier model separates URLs into benign and multiple categories of attacks (phishing, malware, spam and defacement). These classifiers are tested on a dataset comprising around 750,000 URLs. The empirical results show that the proposed model works well in malicious URL detection. The binary classifier provides 95.145% accuracy and 96.844% precision whereas the multi-class classifier provides 94.69% accuracy and 96.234% precision. Also, the comparison results show that the proposed model outperforms leading supervised machine learning algorithms in malicious URL detection.