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

• Published in: 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) pp. 1127 - 1130
• Main Authors: Samuel Raj, R. Joshua, Anantha Babu, S., L, Helen Josephine V, M, Varalatchoumy, Kathirvel, C
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29-03-2022
• Subjects: Binary Classification; Computational modeling; Data visualization; K-Nearest Neighbor; Machine learning; Multiclass classification; Phishing; Random Forest; Social networking (online); Statistical Methods; Uniform resource locators; Unsolicited e-mail
• DOI: 10.1109/ICCMC53470.2022.9754005

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.