A SEIS model for propagation of random jamming attacks in wireless sensor networks

Abstract

This paper describes the utilization of epidemiological models, usually employed for malware propagation, to study the effects of random jamming attacks, which can affect the physical and MAC/link layers of all nodes in a wireless sensor network, regardless of the complexity and computing power of the devices. The random jamming term considers both the more classical approach of interfering signals, focusing on the physical level of the systems, and the cybersecurity approach that includes the attacks generated in upper layers, mainly in the MAC/link layer, producing the same effect on the communication channel. We propose, as a preliminary modelling task, the epidemiological mathematical model Susceptible–Exposed–Infected–Susceptible (SEIS), and analyze the basic reproductive number, the infection rate, the average incubation time and the average infection time.