The SEIRS-NIMFA epidemiological model for malware propagation analysis in IoT networks

With the rapid advancement of Internet of Things networks and its significant cybersecurity challenges, the proposal of models capable of studying malware propagation within these structures has become highly relevant. This paper aims to formulate and implement an SEIRS-NIMFA model to analyze the dissemination of malware infections with a latency period. To accomplish this, we mathematically articulated an SEIRS epidemiological model using an individual-based approach and implemented it using Python. In addition, this paper examines how varying the network size and density, the initially infected device, and several model parameters influence the propagation dynamics. Moreover, to address the Markov chain approach's high temporal and spatial complexity, we use the n-intertwined mean-field approximation method. Our findings demonstrate that our proposal can effectively aid decision-making in implementing security measures in real-world situations. Finally, our proposal and its implementation are open to further enhancements, broadening their potential applications.