# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 6 Conclusions  
The smart factory environment has been transformed into an IIoT environment, which is an interconnected and open approach. This has resulted in many manufacturing plants becoming vulnerable to cyberattacks over the years. Most of these cyberattacks have been carried out by malware. To explain these cyberattacks, we reviewed general cybersecurity threats, the types of cyberattacks that could occur in smart factories, and recent cases of malware-based attacks targeting the manufacturing industry. Cyberattacks targeting smart factories may keep various control systems from performing their functions, without ensuring availability. Furthermore, the implementation of security functions is very important as it can directly lead to physical damage. Therefore, as a measure to counter these cyberattacks, we proposed an edge computing and deep learning-based malware detection system after explaining the architecture composed of three layers and the four core functions required for edge-based deep learning. We evaluated the performance of the model through experiments on the Malimg dataset and achieved an overall classification accuracy of 98.93%, precision of 98.93%, recall of 98.93%, and F1-score of 98.92%. Therefore, it is possible to build a smart factory IIoT industry environment that is safer against malware attacks using this proposed CNN-based malware detection solution.  
Finally, the malware detection and prevention techniques proposed in many studies do not provide a complete solution to various types of malware attacks. Some of the techniques only work for certain types of attacks. Therefore, additional research is required on detection techniques for new malware variants and varieties of malware for smart factory IIoT security in the future. Accordingly, we plan to conduct research on approaches that can detect smart factory IIoT target attacks more accurately by specifying deep learning models created at the edge node based on various types of feature datasets.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Division** & **[98]** & **Our Model** \\ \hline Input image & Color (224 \(\times\) 224 pixel) & Gray (112 \(\times\) 112 pixel) \\ Epochs & 1000 & 40 \\ Dropout & 0.5 & 0.45 \\ Batch size & 64 & 32 \\ Deep learning architecture & VGG16 + Pyramid CNN & CNN \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison with model using VGG16 + Pyramid CNN architecture [98].  
**Author Contributions:** Conceptualization, H.-m.K.; Data curation, H.-m.K.; Formal analysis, H.-m.K.; Methodology, H.-m.K.; Project administration, K.-h.L.; Resources, H.-m.K.; Software, H.-m.K.; Supervision, K.-h.L.; Validation, H.-m.K.; Visualization, H.-m.K.; Writing--original draft, H.-m.K.; Writing--review & editing, H.-m.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.