## 3 System Model  
### Proposed Polymorphic Heterogeneous Security Architecture  
To address the security and privacy challenges faced by edge nodes in cloud-edge collaboration ICSs, we propose a privacy-preserving polymorphic heterogeneous security architecture. This architecture enhances the DHR framework by incorporating privacy-preserving arbitration mechanisms and executor scheduling strategies based on credibility and heterogeneity metrics. Our design specifically targets false data injection attacks against edge node executors and confidentiality attacks against the arbitration module, ensuring both security and privacy protection within the edge nodes. An edge node in the proposed architecture consists of four primary components: input agent, heterogeneous executor set, arbitration and scheduling module, and output agent, as illustrated in Figure 2. The input agent receives and replicates incoming data, distributing identical inputs to each heterogeneous executor within the executor set. The heterogeneous executor set comprises multiple functionally equivalent yet structurally diverse executors arranged in a parallel configuration, connecting both the input agent and the arbitration and scheduling module. Despite their structural differences, these executors are designed to be functionally equivalent, theoretically producing identical outputs when processing the same inputs under normal operating conditions. The arbitration and scheduling module compares the outputs generated by each executor in response to identical inputs. When all outputs are consistent, the unified result is transmitted through the output agent as the system's response. However, when inconsistencies are detected, the module initiates a scheduling process to deactivate potentially compromised executors and activate new heterogeneous executors from the available pool. The output agent serves as the final interface, securely delivering validated control commands or processed data to their intended destinations. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Work** & **Methodology** & **Privacy Protection** & **Application Domain** \\ \hline [PERSON] et al. [44] & Multi-level 2-mode & \multirow{2}{*}{Not addressed} & Industrial applications \\  & judgment algorithm & & (real-time constraints) \\ \hline [PERSON] et al. [46] & Active defense arbitration with & \multirow{2}{*}{Not addressed} & Mimic IoT \\  & adaptive anomaly sensing & & \\ \hline [PERSON] et al. [47] & Location-aware metrics & \multirow{2}{*}{Not addressed} & General DHR systems \\  & (common-mode index, & \multirow{2}{*}{Not addressed} & General DHR systems \\  & transfer probability) & & \\ \hline [PERSON] et al. [48] & HSelect approach with & \multirow{2}{*}{Not addressed} & N-versioning systems \\  & file/block-level code & & \\  & change detection & & \\ \hline [PERSON] et al. [49] & Historical credibility and & \multirow{2}{*}{Not addressed} & General DHR systems \\  & dissimilarity clustering (HCDC) & & \\ \hline [PERSON] et al. [50] & Mimic scoring voting algorithm & \multirow{2}{*}{Not addressed} & General DHR systems with \\  & with heterogeneity measures & & limited executors \\ \hline [PERSON] et al. [51] & Polymorphic multi-mode decision & \multirow{2}{*}{Not addressed} & General DHR systems \\  & with AdaBoost classifiers & & \\ \hline
**Our Work** & **Privacy-preserving arbitration** & \multirow{2}{*}{**Adaptive privacy budget**} & \multirow{2}{*}{**Cloud–edge collaboration ICS**} \\  & **with a hybrid event–time** & & \\  & **triggered scheduling strategy** & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of dynamic heterogeneous redundancy research. In the context of cloud-edge collaboration ICSs, our architecture facilitates secure and private bidirectional data flow across the device-edge-cloud continuum.