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SCIRD: Revealing Infection of malicious software in Edge Computing-Enabled IoT Networks

Computers, Materials & Continua 2024年第5期79卷 2743-2769页

作者： Jiehao Ye Wen Cheng Xiaolong Liu Wenyi Zhu Xuan’ang Wu Shigen Shen School of Information Engineering Huzhou UniversityHuzhou313000China

The Internet of Things(IoT)has characteristics such as node mobility,node heterogeneity,link heterogeneity,and topology *** the face of the IoT characteristics and the explosive growth of IoT nodes,which brings about large-scale data processing requirements,edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service ***,the defense mechanism of Edge Computing-enabled IoT Nodes(ECIoTNs)is still weak due to their limited resources,so that they are susceptible to malicious software spread,which can compromise data confidentiality and network service *** this situation,we put forward an epidemiology-based susceptible-curb-infectious-removed-dead(SCIRD)***,we analyze the dynamics of ECIoTNs with different infection levels under different initial conditions to obtain the dynamic differential ***,we establish the presence of equilibrium states in the SCIRD ***,we conduct an analysis of the model’s stability and examine the conditions under which malicious software will either spread or disappear within Edge Computing-enabled IoT(ECIoT)***,we validate the efficacy and superiority of the SCIRD model through MATLAB *** research findings offer a theoretical foundation for suppressing the propagation of malicious software in ECIoT *** experimental results indicate that the theoretical SCIRD model has instructive significance,deeply revealing the principles of malicious software propagation in ECIoT *** study solves a challenging security problem of ECIoT networks by determining the malicious software propagation threshold,which lays the foundation for buildingmore secure and reliable ECIoT networks.

关键词： Edge computing Internet of Things malicious software propagation model heterogeneity

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