Analysis of the core research for vendor email compromise filtering model using machine learning

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dc.contributor.authorZibarov, Dmytro
dc.contributor.authorKozlenko, Oleh
dc.date.accessioned2023-11-22T10:57:32Z
dc.date.available2023-11-22T10:57:32Z
dc.date.issued2023
dc.description.abstractVendor email compromise became one of most sophisticated types of social engineering attacks. Strengths of this malicious activity rely on basis of impersonating vendor that company working with. Thus, it is easy for attacker to exploit this trust for doing different type of data exfiltration or ransom. To mitigate risks, that come with these challenges, information security specialist should consider using different types of approaches, including machine learning, to identify anomalies in email, so further damages can be prevented. The purpose of this work lies in the identification of optimal approach for VEC-style attacks detection and optimizing these approaches with least amount of falsepositive (FP) parameters. The object of this research is different methods of text processing algorithms, including machine learning methods for detecting VEC emails. The subject of research in this paper mainly considers impact of mentioned text processing algorithms and its relation with efficiency of VEC email classification, identifying most effective approach and, also, how to improve results of such detections. Results of this paper consists of details for VEC-email attacks detection, challenges that comes with different approaches and proposed solution, that lies in using text processing techniques and agentrelated approach with main sphere of implication – machine-learning systems, that are used for identifying social-engineering attacks through email.uk
dc.format.pagerangePp. 87-90uk
dc.identifier.citationZibarov, D. Аnalysis of the core research for vendor email compromise filtering model using machine learning / Dmytro Zibarov, Oleh Kozlenko // Theoretical and Applied Cybersecurity : scientific journal. – 2023. – Vol. 5, Iss. 1. – Pp. 87–90. – Bibliogr. 6 ref.uk
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132023.1.284121
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/62376
dc.language.isoenuk
dc.publisherIgor Sikorsky Kyiv Polytechnic Instituteuk
dc.publisher.placeKyivuk
dc.relation.ispartofTheoretical and Applied Cybersecurity: scientific journal, Vol. 5, No. 1uk
dc.subjectVECuk
dc.subjectemailuk
dc.subjectmachine learninguk
dc.subjectmalicious activityuk
dc.subject.udc004.056uk
dc.titleAnalysis of the core research for vendor email compromise filtering model using machine learninguk
dc.typeArticleuk

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 5, No. 1