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The paper presents an approach to enhancing the efficiency of social engineering attack detection through the development of comprehensive models. Recognizing that social engineering exploits both human psychology and technical vulnerabilities, the study integrates insights from psychology, sociology, and cybersecurity. It explores various forms of social engineering attacks, including phishing, pretexting, and baiting, and emphasizes the importance of accounting for attack vectors, target characteristics, and psychological triggers in model creation. The proposed models cover the entire lifecycle of an attack from reconnaissance to post-attack analysis and incorporate behavioral and psychological aspects crucial to understanding and mitigating these threats. The resulting models aim to improve prediction accuracy, enhance training, and bolster defenses against social engineering attacks.
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Lysenko et al. (2024) studied this question.
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