• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.304-305
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• 2018

Recently, Industrial-IoT (IIoT) solutions accounted for up to 55% in 2016 and technological innovation and various new business models are being developed. In this paper, apply IIoT device in various environments and implement an ontology system that can interwork with block type IIoT device to easily add / change / delete sensor. The proposed system consists of IIoT device, block-type module, and ontology server. When the block-type module is connected to the IIoT device, the appropriate driver is installed and the firmware is downloaded through the ontology server. Even if a block is added / changed / deleted, it can be updated automatically. Through experiments, we confirmed that the normal operation of the server and the updating and downloading of software are implemented normally.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.197-202
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• 2022

IoT (Internet of Things) devices are being attacked by malware due to many security vulnerabilities, such as the use of weak IDs/passwords and unauthenticated firmware updates. However, due to the diversity of CPU architectures, it is difficult to set up a malware analysis environment and design features. In this paper, we design time series features using the byte sequence of executable files to represent independent features of CPU architectures, and analyze them using recurrent neural networks. The proposed feature is a fixed-length time series pattern extracted from the byte sequence by calculating partial entropy and applying linear interpolation. Temporary changes in the extracted feature are analyzed by RNN and LSTM. In the experiment, the IoT malware detection showed high performance, while low performance was analyzed in the malware family classification. When the entropy patterns for each malware family were compared visually, the Tsunami and Gafgyt families showed similar patterns, resulting in low performance. LSTM is more suitable than RNN for learning temporal changes in the proposed malware features.