Implementing Multiclass Classification to find the Optimal Machine Learning Model for Forecasting Malicious URLs
R. Joshua Samuel Raj, S. Anantha Babu, Helen Josephine V L, M. Varalatchoumy, C Kathirvel · 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) · 2022
Web attacks such as spamming, phishing, and malware are common on the Internet. When an unsuspecting user hits the URL, the user becomes a victim of the assaults, which have significant consequences for commercial, finance, and social networking sites. Lexical features, host-based features, content-based features, DNS features, popularity features, and other discriminative features are used to generate a decent feature representation of the URL. URL dataset is collected from ISCX-URL. The goal of this research is to create a multi-class classification model that can categorise URLs as a possible threat to system security by combining several criteria to get the optimal Machine Learning Model.