Malicious URL Detection Using Machine Learning

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Author(s): Ferhat Ozgur Catak (Simula Research Laboratory, Oslo, Norway), Kevser Sahinbas (Istanbul Medipol University, Turkey)and Volkan Dörtkardeş (Şahıs Adına, Turkey)
Copyright: 2021
Pages: 21
Source title: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems
Source Author(s)/Editor(s): Ashish Kumar Luhach (The PNG University of Technology, Papua New Guinea)and Atilla Elçi (Hasan Kalyoncu University, Turkey)
DOI: 10.4018/978-1-7998-5101-1.ch008

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Systems and Software Engineering

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Abstract

Recently, with the increase in Internet usage, cybersecurity has been a significant challenge for computer systems. Different malicious URLs emit different malicious software and try to capture user information. Signature-based approaches have often been used to detect such websites and detected malicious URLs have been attempted to restrict access by using various security components. This chapter proposes using host-based and lexical features of the associated URLs to better improve the performance of classifiers for detecting malicious web sites. Random forest models and gradient boosting classifier are applied to create a URL classifier using URL string attributes as features. The highest accuracy was achieved by random forest as 98.6%. The results show that being able to identify malicious websites based on URL alone and classify them as spam URLs without relying on page content will result in significant resource savings as well as safe browsing experience for the user.

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Malicious URL Detection Using Machine Learning

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