Optimizing Cybersecurity Budgets in Financial Networks: A Comparative Study of Genetic Algorithms and Trust-Region Methods

Abstract This study presents a comparative analysis of genetic algorithms (GA) and trust-region constrained optimization (trust-constr) for optimizing cybersecurity budgets in interconnected financial networks. Using a monte carlo simulation with 10,000 iterations, we evaluated attack probabilities, residual risks, and cost structures across 15 financial nodes, including banks, fintech firms, and data centers. Our findings reveal that the trust-constr method outperforms the genetic algorithm (GA), delivering a 10.5% reduction in total costs ($52.1M compared to $58.2M) and a 15.6% decrease in residual risk (0.0038 vs. 0.0045), all while maintaining strict compliance with budgetary constraints. In contrast, the GA violated financial constraints in 26.7% of cases (4/15 nodes), including a critical violation where Bank_K’s budget exceeded regulatory limits by 189% ($10.64M vs. $3.68M cap). These findings highlight that gradient-based methods are preferable for constraint sensitive financial applications, whereas GA may be reserved for exploratory, non-convex problem spaces. This work provides cybersecurity managers and policymakers with empirically validated guidelines for selecting optimization techniques based on problem structure and regulatory requirements.