Real-time Attack-recovery for Cyber-physical Systems Using Linear-quadratic Regulator
Real-time Attack-recovery for Cyber-physical Systems Using Linear-quadratic Regulator
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DOI：
10.1145/3477010
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发表时间：
2021-09
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通讯作者：
Lin Zhang;Pengyuan Lu;Fanxin Kong;Xin Chen;O. Sokolsky;Insup Lee
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作者：
Lin Zhang;Pengyuan Lu;Fanxin Kong;Xin Chen;O. Sokolsky;Insup Lee
网络物理系统(CP)的自主性和连接性不断增强，随之而来的是新的安全漏洞，恶意攻击者很容易利用这些漏洞来欺骗系统执行危险的操作。虽然现有的绝大多数工作都集中在攻击预防和检测上，但关键问题是“检测到攻击后怎么办？”这个问题很少引起注意，尽管它的重要性被强调为需要减轻甚至消除对系统的攻击影响。在本文中，我们研究了这一攻击响应问题，并提出了一种新的实时恢复方法来保护CPS。首先，这项工作的核心部件是一个使用线性二次型调节器(LQR)的恢复控制计算器，它具有定时和安全约束。该组件可以平稳地将受控制的物理系统引导回在安全期限之前设置的目标状态，并且一旦它被驱动到该集合中，就保持该集合中的系统状态。在此基础上，提出了一种基于乘子交替方向法(ADMM)的算法，该算法能够快速解决基于LQR的恢复问题。其次，攻击恢复计算的支持组件包括检查指针、状态重构器和期限估计器。为了分别实现这三个部分，我们提出了(I)基于滑动窗口的检查点协议，它管理足够的可信数据；(Ii)状态重建方法，它使用检查点数据来估计当前的系统状态；(Iii)基于可达性的方法，保守地估计安全截止日期。最后，我们实现了我们的方法，并在基于5个CPS模拟器和3种类型的传感器攻击的15个实验场景中展示了它的有效性。
The increasing autonomy and connectivity in cyber-physical systems (CPS) come with new security vulnerabilities that are easily exploitable by malicious attackers to spoof a system to perform dangerous actions. While the vast majority of existing works focus on attack prevention and detection, the key question is “what to do after detecting an attack?”. This problem attracts fairly rare attention though its significance is emphasized by the need to mitigate or even eliminate attack impacts on a system. In this article, we study this attack response problem and propose novel real-time recovery for securing CPS. First, this work’s core component is a recovery control calculator using a Linear-Quadratic Regulator (LQR) with timing and safety constraints. This component can smoothly steer back a physical system under control to a target state set before a safe deadline and maintain the system state in the set once it is driven to it. We further propose an Alternating Direction Method of Multipliers (ADMM) based algorithm that can fast solve the LQR-based recovery problem. Second, supporting components for the attack recovery computation include a checkpointer, a state reconstructor, and a deadline estimator. To realize these components respectively, we propose (i) a sliding-window-based checkpointing protocol that governs sufficient trustworthy data, (ii) a state reconstruction approach that uses the checkpointed data to estimate the current system state, and (iii) a reachability-based approach to conservatively estimate a safe deadline. Finally, we implement our approach and demonstrate its effectiveness in dealing with totally 15 experimental scenarios which are designed based on 5 CPS simulators and 3 types of sensor attacks.