Iranian Journal of Numerical Analysis and Optimization

Iranian Journal of Numerical Analysis and Optimization

A multi-layer SEIRS-V network epidemic model for IoT malware propagation with heterogeneous device types and patch dynamics

Document Type : Research Article

Author
Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
10.22067/ijnao.2026.97856.1846
Abstract
The Internet of Things (IoT) ecosystem comprises billions of heterogeneous devices with diverse hardware architectures, operating systems, vulnerability profiles, and patch management capabilities, creating a complex and dynamic attack surface for malware propagation. This paper develops a comprehensive multi-layer network epidemic model for characterizing malware spread in heterogeneous IoT networks. The proposed framework integrates three key dimensions: (1) a Susceptible-Exposed-Infected-Recovered-Susceptible with Vaccination (SEIRS-V) compartmental structure capturing device-level infection states and patching status, (2) a multi-layer network topology distinguishing between physical proximity, logical connectivity, and protocolbased communication graphs, and (3) device-type heterogeneity with class-specific parameters for vulnerability, infectiousness, and recovery rates. The hybrid formulation yields a system of coupled differential equations that admits analytical expressions for the basic reproduction number R0, type-specific epidemic thresholds, and equilibrium prevalence. Numerical simulations calibrated with empirical IoT device data from Shodan and Censys demonstrate that physical proximity layers increase early-stage propagation speed by 28-43% compared to logical connectivity alone, while device-type heterogeneity reduces the critical epidemic threshold by 31-47% relative to homogeneous assumptions. The model provides security analysts with quantitative tools for identifying critical infection pathways, prioritizing device classes for patching, and evaluating network segmentation strategies.
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Articles in Press, Accepted Manuscript
Available Online from 03 July 2026