Modelling the Spread of Botnet Malware in IoT-Based Wireless Sensor Networks

The propagation approach of a botnet largely dictates its formation, establishing a foundation of bots for future exploitation. The chosen propagation method determines the attack surface, and consequently, the degree of network penetration, as well as the overall size and the eventual attack potency. It is therefore essential to understand propagation behaviours and influential factors in order to better secure vulnerable systems. Whilst botnet propagation is generally well-studied, newer technologies like IoT have unique characteristics which are yet to be thoroughly explored. In this paper, we apply the principles of epidemic modelling to IoT networks consisting of wireless sensor nodes. We build IoT-SIS, a novel propagation model which considers the impact of IoT-specific characteristics like limited processing power, energy restrictions, and node density on the formation of a botnet. Focusing on worm-based propagation, this model is used to explore the dynamics of spread using numerical simulations and the Monte Carlo method, and to discuss the real-life implications of our findings

Mathematical Modeling of worm infection on computer in a Network: Case study in the Computer Laboratory, Mathematics Department, Diponegoro University, Indonesia

Worm infection were an infection that attack a computer, it work by multiplied itself after got into a computer and made it over work and caused a computer to slowing down. Worm spreading infection describe by nonlinear mathematic model form with VEISV (Vulnerable, Exposed, Infected, Secured) as the model. Worm free equilibrium and endemic equilibrium were calculated to obtain the stability analysis, and numeric solution were performed using data from Computer Laboratory, Mathematics Department of Faculty of Sciences and Mathematics, Diponegoro University using Runge-Kutta fourth-order method. From the result of stability analysis we obtained that worm free equilibrium were not stable and endemic equilibrium were locally asymptotically stable, and from the result of numeric solution every class proportion from model were obtained