\begin{abstract}

Trust is paramount in \ac{cpss}, and particularly within \ac{scs} across healthcare, transportation, and infrastructure domains. However, accurately assessing and quantifying trust remains a challenge due to complex  dynamic environments and uncertainties in indirect observations. Current approaches rely on indirect observations of a system to determine system state (normal, under attack, defective), but these observations can be misleading. Therefore, there is a need to develop a trust-based mechanism that considers the dynamic nature of \ac{cpss} and provides reliable quantification of their trust levels. This paper proposes a novel methodology to address this problem. By analyzing time-series representing system activity and evaluating their compliance with predefined temporal properties like \ac{stl}, we aim to quantify trust by leveraging \ac{sl}. Our proposed  trust-based mechanism can enhance the reactiveness of the system and guide the user in their interaction with critical systems, improving reliability in dynamic environments.




% Addressing this gap necessitates innovative approaches that integrate physics-based cyber controls and sophisticated analytical techniques that aggregate and analyze indirect observations, thereby enabling the establishment of robust trust frameworks essential for ensuring the reliability and responsiveness of CPSs.  Current methodologies often fall short in providing reliable trust metrics, leaving CPSs vulnerable to potential failures and disruptions. indirect observations of the system can carry uncertainty and be misleading in ascertaining the true state of the system.




% given indi obser with uncertainties output a trust verdict using cuml fusion in sub logi
% application: Cyber-security for cyber-physical systems

% proof by contradiction
% robust state estimation
\end{abstract}