###### Abstract  
Due to the recent rapid development of the ICT (Information and Communications Technology) field, the industrial sector is also experiencing rapid informatization. As a result, malware targeting information leakage and financial gain are increasingly found within IIoT (the Industrial Internet of Things). Moreover, the number of malware variants is rapidly increasing. Therefore, there is a pressing need for a safe and preemptive malware detection method capable of responding to these rapid changes. The existing malware detection method relies on specific byte sequence inclusion in a binary file. However, this method faces challenges in impacting the system or detecting variant malware. In this paper, we propose a data augmentation method based on an adversarial generative neural network to maintain a secure system and acquire necessary learning data. Specifically, we introduce a digital twin environment to safeguard systems and data. The proposed system creates fixed-size images from malware binaries in the virtual environment of the digital twin. Additionally, it generates new malware through an adversarial generative neural network. The image information produced in this manner is then employed for malware detection through deep learning. As a result, the detection performance, in preparation for the emergence of new malware, demonstrated high accuracy, exceeding 97%. digital twin; IIoT; malware; generative adversarial network; image interpolation +
Footnote †: journal: Medical  
certain value changes in the real world and virtually simulates the dynamic aspects of real objects [4]. Among the technologies using virtual machines, one method for detecting malware involves executing a target file directly in a virtual machine to analyze its behavior. By employing the digital twin in this manner, the code operates in an isolated space, independent of the actual system. In other words, utilizing the digital twin environment does not have any adverse effects on the actual system. Furthermore, even if the system becomes infected with malware, it can be quickly reset to its initial state. This capability enables the efficient examination of multiple codes in succession. Machine learning technology is also applied to the detection of malware. As a result, researchers are exploring the use of image recognition technology to convert executable files into images and subsequently determine or classify them as malicious [5]. Deep learning eliminates the need for preprocessing to extract features from images, making it an efficient approach for detecting and analyzing malware. Improving the performance of deep learning models requires a significant amount of data. Obtaining sufficient data that aligns with the learning goals of the desired model, especially in the case of analyzing malware, poses a challenge. Insufficient data hinders the effective training of malware classification and detection models. To overcome this difficulty, data augmentation can be applied. Data augmentation techniques are widely used across different fields, leading to a variety of methods [6; 7]. In this paper, we propose a data augmentation method for malware using image conversion and adversarial generative neural networks in a digital twin environment. The proposed method consists of two parts: the image conversion method and data generation. The image conversion method suggests suitable quality and size for detection using deep learning. Subsequently, an adversarial generative neural network is employed to augment the missing data. Additionally, the generated images are trained with CNN to assess their performance. This generative network predicts and prevents the generation of malware in the future through digital twins. By utilizing real data, a digital twin can proactively block or defend against potential damage by simulating the virtual world. This approach enables the detection of combinations that could potentially harm the system through advanced simulations. Malware often involves partial modifications of existing code.