Ah Reum Kang

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%.