Modeling and Analyzing the Spatial-Temporal Propagation of Malware in Mobile Wearable IoT Networks

Wearable Internet of Things (IoT) devices are easily compromised by malware due to their security vulnerabilities. The bots infected by malware may continue to infect healthy neighbor devices through wireless communication technology in mobile wearable IoT networks (WIoT). All bots form a botnet which eventually leads to a series of malicious attacks. Therefore, it is necessary to predict the dynamic malware propagation path between wearable devices, which can help provide target immunization measures on devices to prevent the formation of botnets. In this article, we capture the local interaction and spatial-temporal propagation behavior of malware utilizing the individual-based cellular automata (CA) model. First, taking into account the mobility of walking users carrying wearable devices in the actual WIoT, we present a human mobility model called Gauss-Markov truncated Levy walk (GM-TLW) to describe the movement patterns of mobile users. Second, based on the moving coordinates of all wearable devices obtained from the GM-TLW mobility model, we leverage the improved CA propagation model to study the time evolution of the number of bots and the spreading spatial distribution of malware. We compare our propagation model with the differential equation model and traditional CA model, and analyze the impact of various parameters on the dynamics of botnet formation using numerical simulations. Finally, detailed simulation results show that the GM-TLW model is more suitable for realistic human mobility scenarios. In addition, the proposed CA-based model is more precise than the differential equation model to modeling the malware propagation and provides a basis for defenders to adopt the optimal malware control strategies.