Dynamics of Multi-Strain Malware Epidemics over Duty-Cycled Wireless Sensor Networks

Insights on the salient features of malicious software spreading over large-scale wireless sensor networks (WSNs) in low-power Internet of Things (IoT) are not only essential to project, but also mitigate the persistent rise in cyber threats. While the analytical findings on single malware spreading dynamics are well-established, the interplay among multiple malware strains with heterogeneous infection rates in power-limited WSNs yet remain unexplored. Inspired by compartmental modeling in epidemiology, we present the mean-field approximation for a novel stochastic epidemic model of two mutually exclusive malware strains spreading over WSNs with sleep/awake modes of energy consumption. Referred as the susceptible-infected by strain 1 or by strain 2-susceptible with duty cycles (SI1I2SD), we then derive the basic reproduction number to characterize the sufficient conditions for the existence and stability of the infection-free and endemic equilibrium states. Simulation results show the predictive capability of the proposed model for energy-efficient WSNs evolving as random geometric graphs against uniformly connected networks.