A NIMFA Epidemiological Model for Analyzing Malware Behavior in IoT Networks

The Internet of Things describes the network of electronic devices that are consistently interconnected via Internet. These devices, which encompass sensors, software, and similar mediums, interact with other devices or systems. As the quantity of these interconnected devices on the Internet has surged dramatically, there has been an increasing interest among certain malicious individuals to exploit these devices for their own gain. A notable instance of this is illustrated by the Mirai Botnet case. To counteract the potential manipulation of these devices by malicious entities, researchers have embarked on proposing models to comprehend the behaviors of these Botnets and their propagation patterns. In this sense, we aim to investigate one of these models, namely the NIMFA model. This model is noteworthy for its impressive scalability, enabling effective modeling of networks with up to 100,000 nodes with quadratic time complexity. Consequently, we propose to design a scalable SIS stochastic epidemiological model based on the N-intertwined Mean-Field Approximation (NIMFA) model for security analysis in IoT networks. Moreover, the outcomes of the NIMFA model closely align with those of other models, such as the Gillespie Simulation Algorithm.