Social Engineering in Cybersecurity: A Survey of Behavioral Threats, Detection Models, and Prevention Strategies

Social engineering is still one of the most widespread and threatening cyber threats, and it is caused more by human vulnerability than technological weaknesses. Driven by the growing sophistication of threats and the absence of collective perception in the discipline, this research combines three general viewpoints: an in-depth expert interview-based analysis, a conceptual framework proposal to operationalize social engineering in cybersecurity, and a mathematical detection model based on a finite state machine. The initial paper emphasizes the essential role of user awareness in preventing threats, with a finding that organizations will tend to prioritize technical measures over staff education. Based on qualitative interviews with cybersecurity professionals, the research determines that socially engineered attacks take advantage of human trust, resulting in credential theft, ransomware attacks, and data breaches. The second paper resolves conceptual ambiguities for the term ”social engineer- ing” through a consideration of its history, suggesting a clear, operational definition, and providing structured comparative models. The third contribution is the development of the Social Engineering Attack Detection Model (SEADM), with an added deterministic finite state machine that classifies attack vectors by communication modes and user responses. This model helps to organize organizational defenses by detecting and stopping social engineering attempts via formalized transitions. Taken together, the results highlight that a multi-faceted approach—integrating awareness, conceptual clarity, and structured detection mecha- nisms—is needed in order to fight the rising threat of social engineering. This intersection of theoretical, conceptual, and pro- cedural innovations presented herein provides a strong platform for both comprehending and countering human-centric cyber threats.