Reducing Organizational Vulnerabilities to Social Engineering via Risk Prediction
DOI:
https://doi.org/10.48047/4eb38277Keywords:
Social engineering, behavioral analysis, attack prediction, security framework, medical, rehabilation, organizational resilience.Abstract
Social Engineering attacks circumvent technological defences and jeopardise organisational security by
manipulating human psychology by taking advantage of authority, urgency, and trust considerations. In order to foresee
and mitigate such attacks, this study presents a behaviorally-driven approach that integrates psychological insights with
Social Network Analysis (SNA). The framework's fundamental component is the BRP algorithm, which divides people
into high-, medium-, and low-risk groups by calculating a Risk Index (RI) for each individual based on behavioural and
network centrality measures. The findings demonstrate how the framework dramatically raises the training completion
rate from 70% to 90% and lowers phishing success rates from 60% to 30%. The study actually emphasises the benefits
of customised actions, updated regulations, and advanced detection technologies that raise security metrics and improve
organisational resilience. This paradigm is especially pertinent to the medical and rehabilitation fields, where maintaining
uninterrupted operations and protecting sensitive patient data are essential. It also offers a proactive, flexible way to
combat these constantly evolving social engineering risks by addressing human vulnerabilities while taking technical
defence into account.
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References
Ahmed, Y. A., Koçer, B., Huda, S., Al-rimy, B. A. S., & Hassan, M. M. (2020). A system call refinement-based enhanced Minimum Redundancy Maximum Relevance method for ransomware early detection. Journal of Network and Computer Applications, 167, 102753.
Ahmed, Y. A. (2020). Automated analysis approach for the detection of high survivable ransomwares. acikerisim.selcuk.edu.tr Baliyan, H., & Prasath, A. R. (2024, June). Enhancing Phishing Website Detection Using Ensemble Machine Learning Models.
In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0 (pp. 1-8). IEEE.
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