Dynamic Stackelberg Games with Epistemic Beliefs for Trace-Driven Evaluation of Defense Strategies in Cybersecurity

In the evolving cybernetic landscape, the development of highly responsive computational models constitutes a critical determinant of system resilience. As adversarial dynamics accelerate and become increasingly non-stationary, models must exhibit ultra-low-latency inference, adaptive contextualization, and autonomous self-optimization. Responsiveness transcends mere computational speed; it embodies algorithmic elasticity and epistemic agility, enabling systems to recalibrate their internal representations in real time under adversarial pressure. Hence, responsiveness emerges as the defining property that sustains homeostatic stability and operational continuity in self-regulating systems.In this study, we introduce a decision-theoretic algorithmic framework for the optimal selection of defensive strategies in safeguarding computational and information infrastructures against dynamic cyber adversaries. The proposed model synthesizes Epistemic Game Theory with the Stackelberg paradigm, facilitating an analytically grounded mechanism for equilibrating defensive efficacy, operational costs, and expected utility under adversarial uncertainty.The formulation models defender–attacker interactions as a Stackelberg game, wherein the defender assumes the role of the strategic leader. This construct is augmented with epistemic belief updating to capture multi-round adaptive behaviour and trace-driven adversarial modeling that reflects empirically observed attack trajectories. The defender dynamically revises probabilistic beliefs regarding the attacker’s typology and anticipated behavioural evolution, thereby enabling real-time adaptive and epistemically informed decision-making.A simulation conducted under empirically realistic conditions—focusing on defense mechanisms against Distributed Denial of Service (DDoS) attacks—demonstrates the operational viability of the framework. Additional evaluation using synthetic data traces encompassing a wider spectrum of attack archetypes substantiates the model’s generality. Results reveal that optimal defensive strategies emerge from the interplay between epistemic updating and the system’s intrinsic reward structure, underscoring the necessity of jointly modeling behavioural dynamics and incentive mechanisms within contemporary cybersecurity decision architectures.