The SEIRS-NIMFA compartmental epidemic model for the analysis of IoT malware propagation

Abstract

With IoT networks' rapid advancement and significant cybersecurity challenges, the proposal and analysis of models capable of studying malware propagation within these structures have become highly relevant. This paper aims to formulate and implement a SEIRS-NIMFA model to analyze the propagation of malware infections with a latency period. To achieve this, a SEIRS model was mathematically described using an individual-based approach and then implemented using Python. This study examines the effects of varying network topology, the initially infected device, and the model parameters on the propagation dynamics. The results demonstrate that this novel model can effectively support the decision-making processes for implementing security measures in real-life scenarios. Furthermore, the model and its implementation are open to further extensions, enhancing their potential applicability.