Analyzing the influence of users, devices, and search engines on viral spread in the social internet of things

The Social Internet of Things (SIoT) seamlessly integrates the Internet of Things (IoT) with social networks, intensifying the interconnections among objects, humans, and their interactions. While SIoT facilitates rapid information access and sharing through search engines, it also increases the risk of computer virus propagation. It is, therefore, critical to understand how viruses propagate in SIoT networks and which factors contribute the most to viral spread. While such understanding is of paramount importance, comprehensive studies on this topic are still limited in the literature. To address this gap, we study in this paper the long-term behavior of viral spread in SIoT, examining the roles of users, devices, and search engines. Specifically, we propose a novel dynamical virus propagation model that accounts for key factors, such as user awareness, device security levels, search engines, and external storage media. In comparison to competing solutions, the proposed model offers a unique perspective on viral spread in SIoT by focusing on multiple influential factors, their interactions, while also considering the inherent characteristics of the SIoT framework. A comprehensive theoretical analysis of the model is conducted to identify patterns and the key aspects of virus propagation in SIoT. To further validate the findings, a virus propagation algorithm is also designed, and multiple simulations are conducted on two real network datasets (Facebook and P2P), demonstrating the validity of the theoretical findings.