Detecting Malicious URL
Fatimah Alkhudair, Mada Alassaf, Rehan Ullah Khan, Shouq Alfarraj · 2020 International Conference on Computing and Information Technology (ICCIT-1441) · 2020
With the ubiquitous use of Internet technology and the rapid development, many of essential life activities (banking, medicine, social networks, etc.) have shifted into Web-based services. As Web-based services became increasingly ubiquitous, they attract cybercriminals through malicious Uniform Resource Locator (URLs) and other ways to perform illegal actions. In recent years, malicious URLs have become an important security issue and an increasingly serious threat to the Web security, it is crucial to detect such threats. In this paper, therefore we applied four machine learning algorithms to detect malicious URLs. The experimental results show that the best performance is achieved by Random Forest algorithm with an accuracy of 96% and 95% during two test phases.