A stochastic Monte Carlo structure is proposed to evaluate security-system effectiveness across multiple airport-security scenarios, using variables such as baseline threat level, screening detection probability, insider-risk contribution, unpredictability measures, staffing quality, and management-response efficiency.
Abstract
This paper develops a technical-scientific study of modern European aviation security management and complements the policy review with a numerical simulation framework in MATLAB. The simulation is not a classified or operational attack model; it is a decision-support model for comparing security management strategies under uncertainty. A stochastic Monte Carlo structure is proposed to evaluate security-system effectiveness across multiple airport-security scenarios, using variables such as baseline threat level, screening detection probability, insider-risk contribution, unpredictability measures, staffing quality, and management-response efficiency. The role of unpredictability is especially relevant because recent qualitative research indicates that European airport stakeholders use variability in controls to complement standard security systems, reduce adversary adaptation, and mitigate insider-threat advantages, though deployment remains insufficiently evaluated in a systematic way. It also includes a workable MATLAB simulation example that can be extended into sensitivity analysis, policy testing, and security performance benchmarking.
The study found that aviation security technologies—including sensor systems, data analysis tools, automated monitoring, cybersecurity measures, passenger behavior analysis, and regulatory adaptations—are generally perceived as less efficient in detecting threats.
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