Design and Analysis of an Individual-Based Model for Malware Propagation on IoT Networks

Abstract

The main goal of this work is to propose a novel compartmental SEA (Susceptible–Exposed–Attacked) model to simulate malware spreading on an IoT (Internet of Things) network. This is a deterministic and individual-based model, whose main novelty compared to others lies in the used of continuous mathematical techniques, such as ordinary differential equations, in the description of local transition rules that define the changes of the states of the devices. These states are given by probability vectors representing the probabilities of being susceptible, exposed and attacked at each step of time. The qualitative study of the model is presented, and several simulations are performed.