Malicious URL detection using machine learning

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dc.authorid0000-0002-8076-3678
dc.contributor.authorÇatak, Ferhat Özgür
dc.contributor.authorŞahinbaş, Kevser
dc.contributor.authorDörtkardeş, Volkan
dc.date.accessioned2023-02-15T10:11:54Z
dc.date.available2023-02-15T10:11:54Z
dc.date.issued2020
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractRecently, 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.
dc.identifier.citationÇatak, F. Ö., Şahinbaş, K. ve Dörtkardeş, V. (2020). Malicious URL detection using machine learning. Artificial Intelligence Paradigms for Smart Cyber-Physical Systems içinde (160-180. ss.). https://doi.org/10.4018/978-1-7998-5101-1.ch008
dc.identifier.doi10.4018/978-1-7998-5101-1.ch008
dc.identifier.endpage180
dc.identifier.isbn9781799851028
dc.identifier.isbn978179985101X
dc.identifier.isbn9781799851011
dc.identifier.scopusqualityN/A
dc.identifier.startpage160
dc.identifier.urihttps://doi.org/10.4018/978-1-7998-5101-1.ch008
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10448
dc.indekslendigikaynakScopus
dc.institutionauthorŞahinbaş, Kevser
dc.language.isoen
dc.publisherIGI Global
dc.relation.ispartofArtificial Intelligence Paradigms for Smart Cyber-Physical Systemsen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMalicious URL
dc.subjectMachine Learning
dc.subjectDetection
dc.titleMalicious URL detection using machine learning
dc.typeBook Chapter

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