Load-Altering Attacks Mitigation using Bayesian-enhanced Game Theory in Active Distribution Networks

This paper addresses the critical challenge of defending active distribution networks against load-altering attacks via a new Bayesian optimization-enhanced Game theory framework. The attackerdefender interaction is modeled as a finite Stackelberg game, where the attacker strategically manipulates load multipliers to maximize voltage violations and system imbalances, while the defender employs topology control to mitigate these impacts. The framework incorporates both snapshot-based and time-series ramp load-altering attack models, enabling a realistic representation of adversarial behavior that evolves over time or occurs instantaneously. Traditional optimization methods struggle with high-dimensional strategy space and lack adaptability to evolving attack patterns. In our framework, Bayesian optimization operates directly within the game-theoretic formulation to efficiently identify near-optimal attacker and defender strategies under limited computational budgets, outperforming evolutionary baselines in terms of convergence and solution quality. This enables the identification of equilibrium strategies that balance attack effectiveness and defense efficiency. Simulations on the IEEE 123-bus system demonstrate that the framework achieves significant reductions in voltage violations and system imbalances compared to heuristic and learning-based approaches. By optimizing attack and defense strategies, the framework enhances system cyber resilience, offering scalable and adaptive solutions to mitigate modern cyber-physical threats