SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization

Abstract

As an infrastructure of Internet of Things, WSNs (Wireless Sensor Networks) play a more and more important role. However, WSNs are vulnerable to malware and malware can destroy their data security and integrity, which motivates us to explore the role of malware propagation in WSNs. First, according to the actual propagation characteristics of malware in the WSNs and the process of the density change of all node types, we propose an epidemiology-based malware propagation model in consideration of a secondary immune mechanism. Then we set up differential equations to describe the propagation model. By solving differential equations, we can obtain two kinds of equilibrium points indicating that the density of all node types tends to be stable in the WSNs. One is to achieve an equilibrium where only susceptible nodes exist in the WSNs. The other is that malware always exists in the WSNs. Moreover, we prove the local and global stability of these two equilibrium points. Eventually, we analyze the influence and effect of the secondary immunity, forgetting mechanism and containment mechanism on malware propagation in the WSNs, and validate the proposed model through simulations.

Generalized Defensive Modeling of Malware Propagation in WSNs Using Atangana–Baleanu– Caputo (ABC) Fractional Derivative

“…They suggest the mechanism of the design, distribution, and maintenance of the sensor network. Further, Ye et al [64] proposed a SIR1R2 model that discussed about the mechanism of secondary immunity. To analyse the spreading behaviour of virus in WSN computed the value of basic reproduction number (R 0 ) and found that if R 0 < 1 the malware will no longer survive in WSN, whereas if R 0 > 1 malware will spread in the entire WSN.…”

Analysis and Control of Malware Mutation Model in Wireless Rechargeable Sensor Network with Charging Delay

“…In Ref. [22], the authors proposed the SIR1R2 with secondary immunity to analyze the feature of malware propagation in WSNs. In Ref.…”

Information Security Decisions with Consideration of Hacker Intrusion Propagation

“…It can not only study the dynamics diffusion process but also position the key nodes and analyze the threshold of virus propagation [14][15][16][17]. The diffusion characteristics of computer network viruses are similar to those of biological viruses, which are latent, infectious, and destructive; thus, the evolution of virus infection among network nodes can be considered, and the SIR model is used to analyze the computer virus diffusion [18][19][20][21][22]. SIR model is introduced to study the mechanism of hacker intrusion diffusion, which has certain innovative significance.…”