期刊
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
卷 18, 期 -, 页码 5302-5315出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2023.3307956
关键词
Malware; patches; Industrial Internet of Things; differential game; double deep Q-networks
This paper proposes a two-layer malware spread-patch model based on IIoT and designs a new algorithm suitable for suppressing the spread of malware. The effectiveness of the model and algorithm is verified through in-depth analysis and numerous comparative experiments.
Industrial Internet of Things (IIoT), which has the capability of perception, monitoring, communication and decision-making, has already exposed more security problems that are easy to be invaded by malware because of many simple edge devices that help smart factories, smart cities and smart homes. In this paper, a two-layer malware spread-patch model I I PV is proposed based on a hybrid patches distribution method according to the simple edge equipments and limited central computer resources of IIoT. The spread process of malware in IIoT was deeply analyzed using differential game and a differential game model was established. Then optimization theory was further used to solve the optimization problem extracted by introducing subjective effort parameters to obtain the optimal control strategies of devices for malware and patches. In addition, we combined the deep reinforcement learning algorithm into the model I I PV to design a new algorithm DD QN - PV suitable for suppressing the spread of malware in IIoT during the experiments. Finally, the effectiveness of model I I PV and algorithm DD QN - PV are verified by numerous comparative experiments.
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