A Fractional Epidemic Model for Multi-malware Attacks in Wireless Sensor Networks

Nowadays, Wireless Sensor Network (WSN) technology is widely used in many applications requiring distributed monitoring and control capabilities. When large WSN infrastructures are involved, the diffusion of malware among nodes becomes a critical issue, requiring a clear understanding of propagation dynamics to guarantee timely countermeasures. By considering that fractional-order models demonstrated to be particularly effective in forecasting the dynamics of complex real-life processes, this study improves the Susceptible-Exposed1-Exposed2-Infected-Recovered (SE E IR) model, derived from the traditional SEIR epidemic theory and used for the early detection of multi-malware activities in WSNs. In particular, a fractional SE E IR epidemic model has been formulated to explain the propagation of multiple malware infections according to a more versatile and realistic view. The model is structured upon a system of fractional differential equations. The basic reproduction number is also determined as a crucial factor for characterizing malware propagation within the network. The model’s free and endemic equilibrium points are calculated, and the stability of the equilibrium points is investigated to find requirements for preventing continuous malware propagation in WSNs. To complement the theoretical analyses, MATLAB is used to perform numerical simulations.