Cybersecurity and Artificial Intelligence: Triad-Based Analysis and Attacks Review

This study aims to expand the understanding of Artificial Intelligence (AI) attack scenarios and develop effective protection mechanisms against them. The triadic principle was used to investigate attacks on traditional systems and AI systems, enhance these attacks using AI, and employ AI for cybers...

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Veröffentlicht in:Cybernetics and information technologies : CIT Jg. 25; H. 3; S. 156 - 185
Hauptverfasser: Veprytska, Olena, Kharchenko, Vyacheslav, Illiashenko, Oleg
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Sofia Sciendo 01.09.2025
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Schlagworte:
Artificial Intelligence (AI)
Countermeasures
Cyberattacks
Cybersecurity
Machine Learning (ML)
ISSN:1314-4081, 1311-9702, 1314-4081
Online-Zugang:Volltext
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Zusammenfassung:This study aims to expand the understanding of Artificial Intelligence (AI) attack scenarios and develop effective protection mechanisms against them. The triadic principle was used to investigate attacks on traditional systems and AI systems, enhance these attacks using AI, and employ AI for cybersecurity defence. By systematically analysing the interactions between these elements, we create a comprehensive set of attack scenarios and corresponding defensive strategies. Current analysis reveals distinct attack patterns and vulnerabilities associated with traditional and AI-based systems. Effective defence mechanisms and strategies were identified and tailored to various attack scenarios, leveraging AI’s capabilities for improved security measures. The findings provide a structured approach to understanding and mitigating AI-related threats in cybersecurity. By mapping out the roles of AI in both attack and defence, this study offers valuable insights for developing advanced tools and methods to assess system security and enhance countermeasures.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1314-4081
1311-9702
1314-4081
DOI:10.2478/cait-2025-0028