Sensitivity Analysis of the Multi Malware Attacks Model in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) technology is extensively utilized in various applications necessitating monitoring and control functionalities. Nonetheless, some unresolved difficulties impede its effective deployment, primarily related to security concerns. This study investigates and analyzes the parameter sensitivity of the Susceptible-Exposed1-Exposed2-Infected-Recovered (SE1E2IR) model, derived from the classical Susceptible-Exposed-Infected-Recovered (SEIR) epidemic theory and used for the early detection of multi-malware activities in WSNs. In this model, two exposed states have been considered based on the assumption of two different types of malware attacks in the network. The primary SE1E2IR model develops a mechanism for the earlier detection of malware attack incidents in WSNs. The model is structured upon a system of ordinary differential equations. The basic reproduction number is also determined as a crucial factor for characterizing malware propagation within the network. It can be helpful to identify the circumstances in which the network remains almost safe or the risk of a malware outbreak. This work offers a fundamental comprehension of sensitivity analysis for the epidemic SE1E2IR model. Furthermore, it examines the efficacy of the parameters and analyzes the impact of different model parameters on malware spread in WSNs. Then, an analytical comparison of the suggested model with existing models is presented. Numerical simulations are run in MATLAB to augment the theoretical analyses.