Implementing Multiclass Classification to find the Optimal Machine Learning Model for Forecasting Malicious URLs

Abstract

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.