Understanding Malware Dynamics in IoT Networks: Dataset Construction Using Mathematical Epidemiology and Complex Networks

The advent of Internet of Things (IoT) devices has given rise to concerns regarding the dissemination of malware. It is of paramount importance to comprehend the behavior of malware in IoT networks in order to devise effective defense strategies. Our study introduces a methodology for constructing datasets that capture the dynamics of malware in IoT networks, combining mathematical epidemiology and complex network theory. The resulting datasets serve as a valuable resource for both researchers and practitioners, facilitating the analysis of malware propagation mechanisms by applying Artificial Intelligence methods, among others. This research contributes to a deeper understanding of malware dynamics in IoT networks, assisting in the design of cybersecurity measures to be applied in this dynamic field.