SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

The Internet of Things (IoT) has characteristics such as node mobility, node heterogeneity, link heterogeneity, and topology heterogeneity. In 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 functions. However, 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 availability. Facing this situation, we put forward an epidemiology -based susceptible -curb -infectious -removed -dead (SCIRD) model. Then, we analyze the dynamics of ECIoTNs with different infection levels under different initial conditions to obtain the dynamic differential equations. Additionally, we establish the presence of equilibrium states in the SCIRD model. Furthermore, 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) networks. Lastly, we validate the efficacy and superiority of the SCIRD model through MATLAB simulations. These research findings offer a theoretical foundation for suppressing the propagation of malicious software in ECIoT networks. The experimental results indicate that the theoretical SCIRD model has instructive significance, deeply revealing the principles of malicious software propagation in ECIoT networks. This study solves a challenging security problem of ECIoT networks by determining the malicious software propagation threshold, which lays the foundation for building more secure and reliable ECIoT networks.