The Application of Machine Learning Algorithms in Classification of Malicious Websites

Sedighi, T., Montasari, R. and Hosseinian Far, A. (2022) The Application of Machine Learning Algorithms in Classification of Malicious Websites. In: Montasari, R., Carroll, F., Mitchell, I., Hara, S. and Bolton-King, R. (eds.) Privacy, Security And Forensics in The Internet of Things (IoT) :. Cham: Springer. pp. 131-147.

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Item Type:	Book Section
Abstract:	This chapter compares three different machine learning techniques, i.e. Gaussian process classification, decision tree classification and support vector classification, based on their ability to learn and detect the attributes of a malicious website. The data used have all been sourced from HTTP headers, WHOIS lookups and DNS records. As a result, this does not require parsing of the website content. The data are first subjected to multiple steps of pre-processing including data formatting, missing value replacement, scaling and principal component analysis.
Creators:	Sedighi, Tabassom, Montasari, Reza and Hosseinian Far, Amin
Editors:	Montasari, Reza, Carroll, Fiona, Mitchell, Ian, Hara, Sukhvinder and Bolton-King, Rachel
Publisher:	Springer
Faculties, Divisions and Institutes:	Faculties > Faculty of Business & Law > Business Systems & Operations University Faculties, Divisions and Research Centres - OLD > Research Centre > Centre for Sustainable Business Practices Research Centres > Centre for Sustainable Business Practices
Date:	1 January 2022
Date Type:	Publication
Page Range:	pp. 131-147
Title of Book:	Privacy, Security And Forensics in The Internet of Things (IoT) :
Place of Publication:	Cham
Number of Pages:	17
Language:	English
ISBN:	978-3-030-91217-8
DOI:	https://doi.org/10.1007/978-3-030-91218-5_6
Status:	Published / Disseminated
Refereed:	Yes
Related URLs:	https://link.springer.com/chapter/10.100... https://pure.northampton.ac.uk/en/public...
URI:	http://nectar.northampton.ac.uk/id/eprint/16962

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