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Recent years have witnessed the fast advance of security research fornetworked dynamical system (NDS). Considering the latest inference attacks thatenable stealthy and precise attacks into NDSs with observation-based learning,this article focuses on a new security aspect, i.e., how to protect controlmechanism secrets from inference attacks, including state information,interaction structure and control laws. We call this security property ascontrol mechanism secrecy, which provides protection of the vulnerabilities inthe control process and fills the defense gap that traditional cyber securitycannot handle. Since the knowledge of control mechanism defines thecapabilities to implement attacks, ensuring control mechanism secrecy needs togo beyond the conventional data privacy to cover both transmissible data andintrinsic models in NDSs. The prime goal of this article is to summarize recentresults of both inference attacks on control mechanism secrets andcountermeasures. We first introduce the basic inference attack methods on thestate and structure of NDSs, respectively, along with their inferenceperformance bounds. Then, the corresponding countermeasures and performancemetrics are given to illustrate how to preserve the control mechanism secrecy.Necessary conditions are derived to guide the secrecy design. Finally, thoroughdiscussions on the control laws and open issues are presented, beckoning futureinvestigation on reliable countermeasure design and tradeoffs between thesecrecy and control performance.

I Can Read Your Mind: Control Mechanism Secrecy of Networked Dynamical Systems under Inference Attacks