  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.
