## 7 Discussion  
In this paper, we present an innovative intelligent detection technology that leverages generative neural networks within a digital twin-based Industrial Internet of Things (IIoT) environment. Moreover, we propose a novel method for detecting malware solely based on images of the malware using convolutional neural networks (CNN), which is a powerful technique employing deep neural networks. Three different datasets were used to test the performance of the proposed system. The performance of the system was measured in three stages. First, in the imaging stage, the detection performance of the 64 \(\times\) 64 bilinear technique was the best. Second, a new malware image was created using the image interpolation method and size determined in the previous experiment, and similarity with the original images was measured. As a result of the measurement, the FID index was 5.4403 and the correlation coefficient was 0.8698, confirming high similarity. Third, the malware detection performance using the generated \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline performance on & 0.989957 & 0.955587 & 0.968748 & 0.968128 \\ the training set & 0.968812 & 0.917035 & 0.927126 & 0.923067 \\ the test set & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Proposed model & 0.973134 & 0.930811 & 0.942937 & 0.940597 \\ [PERSON] et al., 2011 [22] & 0.8397 & - & - & - \\ [PERSON]. \& [PERSON], 2016 [23] & 0.962 & 0.917158 & 0.921102 & 0.919126 \\ [PERSON] et al., 2009 [26] & 0.96 & - & - & - \\ [PERSON]. \& [PERSON], 2018 [27] & 0.9299 & - & - & - \\ [PERSON] et al., 2020 [28] & 0.92 & - & - & 0.92 \\ [PERSON] \& [PERSON], 2015 [29] & 0.995785 & - & - & - \\ [PERSON] et al., 2017 [30] & 0.826 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison with existing research. malware image was measured. The performance measurement result was detected with an accuracy of 0.97, and was measured with a precision of 0.93, a recall of 0.94, and an f1-score of 0.94. This confirmed a higher level of performance than previous studies. The proposed system does not analyze the data collected in the IIoT environment on the user's system. Instead, it utilizes the digital twin to analyze malware in the digital space. As a result, it does not adversely affect the actual system, providing a safer and more secure analysis environment. Additionally, the system offers the advantage of quick initialization if any problem occurs in the digital space. By converting malware into images that reflect their characteristics, the proposed system eliminates the need to execute or directly analyze the code, minimizing potential risks. Leveraging generative adversarial networks allows for the generation of synthetic malware, which enhances the efficiency of the analysis process. The unpredictable nature of when and in what form malware will emerge poses a challenge for traditional detection methods. However, in the digital twin environment, the proposed system can quickly respond to new forms of malware by generating and analyzing them based on existing malware. This adaptability and responsiveness make the system well-suited for addressing emerging threats in the digital space. The table provided below (Table 8) illustrates the datasets that will be contrasted with the research findings presented in Table 7. In this paper, the Mal60 dataset, the Malimg dataset and the VXHeaven dataset were employed. To enable an objective comparison of studies, we utilized datasets from prior research. The datasets from earlier studies predominantly comprise data made available from 2010 to 2015. This study has certain limitations. During the image downsizing process aimed at reducing computational complexity, some intricate characteristics of the malicious code may diminish.