Toward Optimal False Data Injection Attack against Self-Triggered Model Predictive Controllers,Advanced Theory and Simulations

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Toward Optimal False Data Injection Attack against Self-Triggered Model Predictive Controllers
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2022-03-13 , DOI: 10.1002/adts.202200025
Ning He ₁ , Kai Ma ₁ , Ruoxia Li ₂

Affiliation

This paper is aimed at exploring the optimal false data injection (FDI) attack against continuous time self-triggered model predictive control (STMPC) systems with sample-and-hold input signals to address the potential security defects. First, the mathematical model of FDI attack against the considered STMPC system is established. Then, the difference between the states of the nominal system and the attacked system is explicitly calculated such that the impact of FDI attacks on the STMPC systems can be quantitatively analyzed. And finally, an efficient and effective algorithm to realize the desired FDI attack is proposed, and in order to maintain the flexibility of the attacker, the designed FDI attack algorithm is developed under different attacking scenarios, including attacking a single control node at each sampling time and attacking multiple control nodes each time. Finally, two simulation experiments are carried out based on a robot system and a cart–damper–spring system to verify the efficacy and optimality of the designed FDI attack strategy.

中文翻译：

针对自触发模型预测控制器的最优错误数据注入攻击

本文旨在探索针对具有采样保持输入信号的连续时间自触发模型预测控制 (STMPC) 系统的最优虚假数据注入 (FDI) 攻击，以解决潜在的安全缺陷。首先，建立了针对所考虑的STMPC系统的FDI攻击数学模型。然后，明确计算名义系统和被攻击系统的状态之间的差异，从而可以定量分析 FDI 攻击对 STMPC 系统的影响。最后，提出了一种高效且有效的算法来实现所需的 FDI 攻击，并且为了保持攻击者的灵活性，在不同的攻击场景下开发了所设计的 FDI 攻击算法，包括在每个采样时间攻击单个控制节点和每次攻击多个控制节点。最后，基于机器人系统和推车-阻尼器-弹簧系统进行了两次仿真实验，验证了设计的 FDI 攻击策略的有效性和最优性。

更新日期：2022-03-13

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