Enhancing Wireless Sensor Network Security Against Multiple Malware Threats: A Nonlinear Differential Equation-Based Modelling Approach

Wireless sensor networks, or WSNs, are made up of many wireless sensor nodes that work together to find and record changes in the environment for a range of uses, such as in business, the military, and homes. However, due to limited resources, sensor nodes are vulnerable to malware, including Trojans, viruses, worms, and malicious signals. This work aims to address the crucial problem of protecting WSNs from malware, especially in situations where several malware types coexist and pose significant risks to network stability. The study examines the simultaneous spread of two types of malware infections, which could be worms and viruses. We developed a system with two infectious states, SEI 1 I 2 R (susceptible, exposed, infectious of type 1, infectious of type 2, and recovered), to illustrate how malware spreads in WSNs. We then explained this approach using differential equations. We derive the basic reproduction number, [Formula: see text], to test the equilibrium and stability of the model. This variable is a crucial parameter for comprehending the propagation of malware. The suggested model is validated by thorough theoretical understanding and computational analysis. The suggested model is further compared with existing models, and the comparative study proves that dividing the infectious population into different classes helps in taking appropriate measures to control their spread with suitable anti-malware software and thus prevents malware propagation. Simulation results confirm that the proposed model exhibits much better malware propagation control than the existing models.