Parameter-Driven Evaluation of Zero Trust Security in Blockchain Networks Under Dynamic Threats

In this study, we investigate a parameterized method to evaluate the Zero Trust (ZT) security model integrated with blockchain networks under dynamic cyberattack conditions. Existing static trust-based security models fail to adapt to evolving adversarial behavior and dynamic attack conditions. To address these deficiencies, a discrete-time simulation model is constructed a discrete-time simulation model is developed to capture dynamic trust evolution, probabilistic attack patterns, and threshold-based access control within a financial network. In this regard, the proposed model considers significant parameters such as the probability of attack, trust decay rate, and access, isolation, and quarantine thresholds to evaluate their impact on network security performance. From the results, a significant correlation is noted between trust, mitigation, adversarial intensity, and policy parameter tuning. For instance, a strict threshold policy results in improved attack mitigation but compromises network participation, while a lenient approach results in improved network participation but compromises network security. The proposed framework is a more adaptive, scalable, and robust approach compared to conventional static approaches in addressing dynamic threats. From the results, optimal tuning of parameters is a fundamental aspect in achieving a balance in security enforcement in blockchain-based zero-trust networks.