Mitigating Malware Propagation in Social Internet of Things Using an Exact Markov-Chain-Based Epidemic Method

Abstract

In the Social Internet of Things (SIoT) environment, malware propagation is attracting more and more attention due to increasing damages. Markov chain models have been used to predict epidemic behavior qualitatively and quantitatively, but most of them model random propagation as a basic multiplicative factor. In this article, we propose an epidemic model Susceptible-Infected without command-Infected with command $(SII^{\prime })$ , and derive an exact Markov chain for SIoT malware propagation. We also employ a Markov chain for an SIoT malware mitigation system that groups random devices alongside those with detected infections during the malware eradication process. This mitigation mechanism operates at the network scale, addressing the risks associated with large-scale SIoT deployments through a strategic, yet assertive, approach of widespread disconnections. Such a system effectively drives down the basic reproduction number to less than 1, preventing malware from gaining dominance over the network—all accomplished without modifying the recovery rate. We conducted experimental simulations of the proposed model’s dynamic predictions, and the experimental results show that the use of an exact Markov chain model better matches the benchmark results of our proposed model and also verifies the different effects of group-based mitigation in different SIoT contexts.