Implementation of Worm Recovery Mechanism in Wireless Sensor Networks using SIR Epidemic Model

Wireless Sensor Networks (WSNs) are highly vulnerable to malware and worm propagationdue to their distributed and resource-constrained nature. Several studies have modeled suchattacks using epidemic-based approaches, including SIR and SIQR models, to understandinfection dynamics and stability conditions within networks. These models demonstrate thatthe spread of infection depends on critical thresholds and system parameters, and theyhighlight the role of recovery, quarantine, and immunization strategies in controllingnetwork-wide infections.Recent research also explores stochastic modeling techniques, such as discrete-time Markovchains and probabilistic simulations, to capture the randomness and uncertainty involved inmalware spread across network topologies. These approaches provide deeper insights intohow infection evolves over time and how system parameters influence network resilience andenergy efficiency.Building on these existing approaches, this work focuses on a simplified SIR-basedsimulation model for analyzing worm propagation in WSNs. Our study emphasizes the roleof recovery mechanisms in reducing infection spread and improving network stability,providing a practical and computationally efficient framework for understanding networksecurity behavior.