This work presents a novel compartmental model to understand the dynamics of malware infections in Internet of Things (IoT) networks through an epidemiological lens. The model incorporates interactions among different compartments: susceptible, infected, recovered, and removed IoT devices, explicitly accounting for both human-mediated and device-to-device transmission mechanisms. A system of ordinary differential equations is formulated to describe the progressive evolution of the network state. Key mathematical analyses include the computation of the equilibrium points, the basic reproduction number, and stability of the equilibrium points. Numerical simulations under different scenarios are conducted to validate theoretical results and to explore the impact of various parameters on malware propagation. The analysis provides valuable insights to enhance the resilience of IoT networks against epidemics and contributes to the design of effective mitigation strategies grounded in mathematical modeling.