具有自适应弹性的模型预测控制用于缓解受调节大坝上的拒绝服务攻击
Model Predictive Control with adaptive resilience for Denial-of-Service Attacks mitigation on a Regulated Dam
AI总结：
针对SCADA面临的控制器—执行器DoS攻击，提出以Hawkes过程强度自适应调节弹性因子的MPC架构，并在Olginate大坝真实数据的两个攻击场景中验证其有效性。
摘要：
近年来，SCADA（监控与数据采集）系统日益成为网络攻击的目标。SCADA不再是孤立的，因为基于网络的应用使战略基础设施暴露于外部连接。在网络战背景下，我们提出一种具有自适应弹性的模型预测控制（MPC）架构，能够在正常运行条件下保证控制性能，并在需要时实现针对DoS（控制器—执行器）攻击的弹性。由于攻击者的目标通常是最大化系统损害，我们假设他们求解一个对抗性最优控制问题。随后，将自适应弹性因子设计为Hawkes过程强度函数的函数；Hawkes过程是一种估计随机事件在时间上发生情况的点过程模型，在滑动窗口上训练以估计下一次攻击的返回时间。我们在一个使用真实数据的真实系统——受调节的科莫湖Olginate大坝——上的2个攻击场景中证明了所得MPC策略的有效性。
英文摘要：
In recent years, SCADA (Supervisory Control and Data Acquisition) systems have increasingly become the target of cyber attacks. SCADAs are no longer isolated, as web-based applications expose strategic infrastructures to the outside world connection. In a cyber-warfare context, we propose a Model Predictive Control (MPC) architecture with adaptive resilience, capable of guaranteeing control performance in normal operating conditions and driving towards resilience against DoS (controller-actuator) attacks when needed. Since the attackers' goal is typically to maximize the system damage, we assume they solve an adversarial optimal control problem. An adaptive resilience factor is then designed as a function of the intensity function of a Hawkes process, a point process model estimating the occurrence of random events in time, trained on a moving window to estimate the return time of the next attack. We demonstrate the resulting MPC strategy's effectiveness in 2 attack scenarios on a real system with actual data, the regulated Olginate dam of Lake Como.
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