Al Zirjawi, Sabah Salam KhaduairKashkool, Hawraa Jaafar MuradIbrahim, Abdullahi AbduAl, Mona Idan Ali2023-06-102023-06-102022Al Zirjawi, S. S. K., Kashkool, H. J. M., Ibrahim, A. A., & Al, M. I. A. (2022). Machine learning algorithms for URLs classification. In 2022 International Conference on Artificial Intelligence of Things (ICAIoT). IEEE.9798350396768https://hdl.handle.net/20.500.12939/3535Phishing is a technique used to collect sensitive data from a user (password or credit card information) for future misuse by posing as a trustworthy source. It often takes advantage of the user's gullibility in ways that the user will not detect at first look, and in the worst-case scenario, the attacker maintains the user's data without the user's awareness. Typically, the URL is the first and simplest piece of information we know about a website. As a result, it is logical to design algorithms for distinguishing harmful from benign URLs. Additionally, accessing and downloading the website's material may be time-consuming and involves the danger of downloading potentially hazardous information. Machine Learning techniques are used to train a model on a collection of URLs specified as a set of characteristics and then predict and categorize the URLs as benign or dangerous. This technology enables us to identify and avoid possibly dangerous URLs shortly.eninfo:eu-repo/semantics/closedAccessFeature ExtractionKNNLogistic RegressionNaïve BayesSVMConference Object2-s2.0-85160514303N/A