# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 4 CPS Application Domains
### Manufacturing CPS
#### 4.2.3 Threat Detection  
Anomaly detection mechanisms in manufacturing CPS are only effective if the non-linear spatial-temporal features of the industrial processing data are considered [25]. In their study, the authors proposed a method based on spatial-temporal modeling (AD-RoSM) for detecting FDIA in ICS [25]. Their proposed scheme employs a neural-based state estimation  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref, Year** & **Framework** & **Gap** & **Contribution** \\ \hline
[68], 2022 & STAR & Imputing the missing entries in spatial-temporal traffic data is challenging & addressed the transport data corruption problem in real-time \\ \hline
[70], 2022 & SG2 VEC & Traditional collision prediction methods were expensive & Proposed an improved future autonomous vehicles accidents prediction method \\ \hline
[69], 2023 & DGCRIN & Missing data imputation methods do not account for the dynamic spatial dependencies of the road network of the diverse data & Designed an improved dynamic imputation method \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Objectives** & **Research Papers** \\ \hline \(<\)\(X_{2}\), \(Y_{5}\) (Industrial-Real-time Decisions), \(Z_{2}\)\(>\) & [34] \\ \hline \(<\)\(X_{2}\), \(Y_{4}\) (Industrial-Resource Allocation), \(Z_{3}\)\(>\) & [24] \\ \hline \(<\)\(X_{2}\), \(Y_{2}\) (Industrial-Anomaly Detection), \(Z_{1}\), \(Z_{3}\)\(>\) & [25; 72; 73] \\ \hline \(<\)\(X_{2}\), \(Y_{3}\) (Industrial-Predictive Maintenance)\(>\) & [74; 75; 76] \\ \hline \(<\)\(X_{2}\), \(Y_{1}\) (Industrial-Data Processing)\(>\) & N/A (No much research conducted in this direction) \\ \hline \(<\)\(X_{2}\), \(Y_{6}\) (Industrial-Multi-modal Data Fusion)\(>\) & N/A (No much research conducted in this direction) \\ \hline \hline \end{tabular}
\end{table}
Table 3: DNNs in Manufacturing CPS.  
model that utilizes CNN for time-related modeling and a mechanism for carrying out space-related modeling. In this way, the spatial-temporal correlations within the process data can be described explicitly. [PERSON] et al. [72] proposed a graph representation-based scheme for the detection of multivariate time series anomalies in highly complex industrial processes. Their proposed model is capable of improving the existing techniques by offering spatial-temporal feature extraction and decision criteria based on spatial-temporal graph modeling with no predefined topological priors and a discriminative decision boundary. HiSTAR was shown to provide the expected anomaly detection performance and anomaly localization outcomes. Likewise, [PERSON] et al. [73] adopted CNN on manufacturing spatial-temporal data to identify abnormal production processes. Their study was based on a pasting process in lead-acid battery production as a case study. The CNN-based approach was designed to recognize abnormal processes by analyzing spatial-temporal data from sensors. These efforts can be mapped to the cube \(<\)\(X_{2}\), \(Y_{2}\), \(Z_{1}\), and \(Z_{3}\)\(>\) as shown in Figure 3 and Table 3.