Critical infrastructure increasingly relies on interconnected cyber-physical systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support real-time incident response because they rely on static assumptions, incomplete vulnerability data, and single-objective risk models that do not adequately capture trade-offs between attack likelihood, impact severity, and system availability. This paper proposes a real-time, adaptive decision-support framework for incident mitigation in critical infrastructure that combines hierarchical system modelling with Bayesian probabilistic reasoning. The framework leverages probabilistic graphical models (Bayesian Networks) constructed from system architecture and vulnerability data, and employs confidence-calibrated exposure estimation to integrate complementary vulnerability scoring metrics under epistemic uncertainty. Mitigation strategies are explored as countermeasure portfolios and refined using multi-objective optimisation to identify Pareto-optimal trade-offs suitable for time- and resource-constrained response scenarios. Frequency-based heuristics are applied to prioritise robust mitigation actions across optimisation runs. The framework is evaluated on three representative cyber-physical attack scenarios, demonstrating its ability to adapt to evolving threats and provide actionable decision support under real-time constraints, thereby enhancing the operational resilience of critical infrastructure.

翻译：关键基础设施日益依赖于相互关联的信息物理系统，其安全事件可能迅速升级为安全与运行故障。现有决策支持方法难以支持实时事件响应，因为它们依赖于静态假设、不完整的漏洞数据以及单目标风险模型，这些模型未能充分捕捉攻击可能性、影响严重性与系统可用性之间的权衡。本文提出一种面向关键基础设施事件缓解的实时自适应决策支持框架，该框架将分层系统建模与贝叶斯概率推理相结合。该框架利用基于系统架构和漏洞数据构建的概率图模型（贝叶斯网络），并采用置信度校准的暴露估计方法，在认知不确定性下整合互补的漏洞评分指标。缓解策略被探索为对策组合，并通过多目标优化进行细化，以识别适用于时间和资源受限响应场景的帕累托最优权衡方案。基于频率的启发式方法被用于在多次优化运行中优先选择稳健的缓解措施。该框架在三个具有代表性的信息物理攻击场景上进行了评估，证明了其能够适应不断演变的威胁，并在实时约束下提供可操作的决策支持，从而增强关键基础设施的运行韧性。