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

Abstract

Socialengineeringisstilloneofthemostwidespread andthreateningcyberthreats,anditiscausedmorebyhu- man 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 con- ceptual framework proposal to operationalize social engineering incybersecurity,andamathematicaldetectionmodelbasedon 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 organizeorganizationaldefensesbydetectingandstoppingsocial 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.Thisintersectionoftheoretical,conceptual,andpro- cedural innovations presented herein provides a strong platform for both comprehending and countering human-centric cyber threats.

Introduction

Social engineering (SE) is a major cybersecurity threat that exploits human psychology rather than technical system flaws. Despite advances in technical defenses, humans remain the weakest link, as attackers manipulate trust, urgency, and authority to bypass security by tricking employees into sharing sensitive information or granting access. SE attacks now constitute about 97% of malware-related incidents, highlighting the critical need to address the behavioral aspects of cybersecurity alongside technical measures.

To combat SE effectively, organizations must integrate technical controls with continuous, role-specific user awareness training, behavioral modeling, and psychological understanding. Models like the Social Engineering Attack Detection Model (SEADM) use structured decision-making (via finite state machines) to guide users in verifying requests and recognizing suspicious behavior. Training approaches incorporating gamification, simulations, and immersive role-play improve long-term resilience by reinforcing good security habits in organizational culture.

Challenges include inconsistent awareness program effectiveness, the complexity of modeling human behavior and emotional manipulation, and the need for adaptable, context-specific defenses. Institutional and regulatory support, combined with private-sector involvement, is essential for large-scale prevention, such as mandated cybersecurity education and stricter compliance standards.

Recent research advocates a holistic, interdisciplinary defense framework combining technical, behavioral, organizational, and policy elements to create a “human firewall” capable of withstanding sophisticated social engineering attacks. However, gaps remain in standardizing definitions, metrics, and adaptable models, requiring further study for scalable, effective solutions.

Conclusion

Social engineering has proven to be one of the most en- during risks in contemporary cybersecurity because it attacks human psychology directly over technological vulnerabilities. In contrast to traditional cyberattacks that take advantage of software vulnerabilities, social engineering attacks play with human trust and behavior, making even most secure systems risky when human users are ill-trained or ignorant. As digital worldsbecomemorecomplexandinterconnected,tacklingthis humanelementisbecomingamoreandmorevitalcomponent of cybersecurity policy.Studies have proven that conventional technical defenses, whilebeingnecessary,arenotenoughbythemselvesto counter manipulation-based attacks. Antivirus software, fire- walls,andintrusiondetectionsystemsareincapableofkeeping track of the sophisticated psychological strategies employedby social engineers. Therefore, user awareness, regular train- ing programs, and behavior-based measures are universally recognized as the best countermeasures. Security Education, Training, and Awareness (SETA) programs and simulated phishingcampaignshavebeenparticularlyusefulinincreasing awareness and lowering susceptibility by employees. Innovative frameworks such as the Social Engineering At- tack Detection Model (SEADM) strengthen defense even fur- ther by providing structured, decision-oriented methodologies that formalize the way people should evaluate and react to dubious requests. Similarly, conceptual elucidations such as the accurate definition of social engineering in cybersecurity (SEiCS) make the phenomenon more practically understood. Thecombinationoftheseinstrumentsassistsorganizations in shifting from reactive to proactive defense strategies, al- lowing timely detection and disruption of social engineering campaigns. Even with these developments, various limitations continue to exist. A significant percentage of the literature that exists today is based upon qualitative data, expert judgments, and anecdotal evidence. Although valuable in nature, such infor- mation is deprived of statistical strength and generalizabilityto enable the formulation of universally applicable counter- measures. In addition, most established frameworks have not yet been tested across various industries or cultural settings, leaving doubts regarding their applicability and efficacy in different organizational settings.The ever-changing threat landscape only makes prevention more challenging. New technologies like AI-driven chatbots, deepfakes, and large-scale IoT networks are presenting new fronts for social engineering. Attackers today use highly targetedandcontext-specifictactics,whereitbecomesincreas- inglydifficulttodistinguishbetweennormalandattacktraffic. In this changing landscape, detection models and awareness programs also need to be updated and improved to remain relevant and useful.Subsequentresearchwillberequiredtoempiricallyvalidate existing models and investigate integrating more advanced technologiesintouserawarenessprograms.Thisencompasses the use of machine learning to detect behavior anomalies, blockchain for data integrity verification, and creating adaptive, game-based training platforms to mimic actual-world attack vectors. Furthermore, measuring awareness and behavioral change will be critical in creating standardized metrics for determining the effectiveness of training interventions in the long term.In summary, social engineering is an inherently people- focused threat in cybersecurity. To deal with it effectively istorequireacomprehensiveapproachthatbridgestechnology protectionwithsubstantiveinvestmentinhumaneducationand behavioralmodeling.Futuredefenseagainstsocialengineering is not in opting between man and machine, but in unifying the two for the creation of strong, security-aware organizations.

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Copyright

Copyright © 2025 Chaitra Morab, Laxmi Naik, Prof. Vinod R Kokitkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.