## 3 Deep Learning-Based Methods  
### Time Series Analysis for Deep Learning  
Time series frequently contain temporal dependencies, causing two otherwise identical points of time to belong to various classes or produce a prediction of various behaviors. The mentioned feature usually renders their analysis more challenging. Moreover, the aim is to capture sophisticated inter-sensor relationships and identify and explain abnormalities deviating from the relationships in question. Deep learning approaches have recently enhanced anomaly detection in high-dimensional time series data (such as sensor data). Nevertheless, the current methods still need to thoroughly learn the structure of the available correlations between variables or utilize them to predict the expected behavior of time series. Deep learning models represent a very effective tool for anomaly detection in time series. Such models learn particular patterns in time series and can identify data outside these patterns as anomalies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Year** & **Study** & **Feature** & **Ref.** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{2020} & Industrial Control Systems: Cyberattack & This paper summarizes common attacks & \\  & & trends and counter-countermeasures & collected in the industrial control system (ICS). & \\  & & & It suggests solutions to the most & \\  & & & prominent ones. & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{2019} & Importance of Patch Management to Avoid & Assesses patch management compliance and & \\  & & Business Vulnerabilities. & risk management in ICSs. & \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{2023} & Implementing Patch Management Process & Presents approaches for solutions and methods & \\  & & & for security patch management in ICSs. & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{2021} & The Role of Security Patch Management in & Presents approaches for risk-based security & \\  & & Vulnerability Management & patch management in ICSs. & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{2021} & Gartner Top 10 Security Projects for & \\  & & 2020–2021. & Provides a joint security project for recent years. & \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{2022} & Prioritizing Vulnerability Patches in Large & Ranks vulnerability fixes based on the & \\  & & Networks & machine’s position within large networks. & \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{2022} & Cybersecurity Awareness in the context of & Reviews cybersecurity awareness in IIoT & \\  & & the Industrial Internet of Things: A Systems & contexts with cybersecurity policies. & \\  & & Literature Review. & \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{2021} & Cybersecurity, Survey & Surveys actual and perceived risks, threats, & \\  & & & information sources, and operational & \\  & & & implementation challenges & \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{2022} & Cybersecurity Threats, Counter Measures & Discusses popular application-layer protocols & \\  & & and Mitigation Techniques on the IOT: & for IOT security. & \\  & & Future Research Directions. & \\ \hline \multirow{2}{*}{10} & \multirow{2}{*}{2022} & An Evaluation Framework for Industrial & Analyzes ICS cyber incidents to highlight & \\  & & Control System Cyber Incidents & protection solutions for ICSs. & \\ \hline \multirow{2}{*}{11} & \multirow{2}{*}{2022} & Cybersecurity Analysis of the Wind Farm & Identifies and analyzes cybersecurity threats, & \\  & & Industrial Control System Based on & with an adequate basis for threat abatement & \\  & & Hierarchical Threat Analysis & and system design through the visualization & \\  & & Model Framework & of threats. & \\ \hline \multirow{2}{*}{12} & \multirow{2}{*}{2022} & The Risk of Cybersecurity for the Power & & \\  & & Stability Control System and Its & \\  & & Test Platform & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with other studies on cybersecurity on ICSs.