I Can Read Your Mind：推理攻击下网络动力系统的控制机制
I Can Read Your Mind: Control Mechanism Secrecy of Networked Dynamical Systems under Inference Attacks
摘要：
近年来，网络动力系统安全性研究取得了长足的进展.考虑到最新的推理攻击可以通过基于观察的学习对NDS进行隐形和精确的攻击，本文重点关注一个新的安全方面，即，如何保护控制机制秘密不受推理攻击，包括状态信息、交互结构和控制律。我们将这种安全属性称为控制机制保密性，它为控制过程中的漏洞提供了保护，填补了传统网络安全无法处理的防御空白。由于控制机制的知识定义了实施攻击的能力，因此确保控制机制的保密性需要超越传统的数据隐私，以涵盖NDS中的可传输数据和内在模型。本文的主要目的是总结最近的结果都推理攻击控制机制的秘密和对策。我们首先介绍了基本的推理攻击方法的状态和结构的NDS，分别与他们的推理性能界限。然后，给出了相应的对策和性能指标，以说明如何保持控制机制的秘密性。导出了指导保密性设计的必要条件。最后，深入讨论的控制规律和开放的问题，召唤未来的研究可靠的对策设计和保密性和控制性能之间的权衡。
英文摘要：
Recent years have witnessed the fast advance of security research for networked dynamical system (NDS). Considering the latest inference attacks that enable stealthy and precise attacks into NDSs with observation-based learning, this article focuses on a new security aspect, i.e., how to protect control mechanism secrets from inference attacks, including state information, interaction structure and control laws. We call this security property as control mechanism secrecy, which provides protection of the vulnerabilities in the control process and fills the defense gap that traditional cyber security cannot handle. Since the knowledge of control mechanism defines the capabilities to implement attacks, ensuring control mechanism secrecy needs to go beyond the conventional data privacy to cover both transmissible data and intrinsic models in NDSs. The prime goal of this article is to summarize recent results of both inference attacks on control mechanism secrets and countermeasures. We first introduce the basic inference attack methods on the state and structure of NDSs, respectively, along with their inference performance bounds. Then, the corresponding countermeasures and performance metrics are given to illustrate how to preserve the control mechanism secrecy. Necessary conditions are derived to guide the secrecy design. Finally, thorough discussions on the control laws and open issues are presented, beckoning future investigation on reliable countermeasure design and tradeoffs between the secrecy and control performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
