IET Communications (Jul 2023)

An epidemic model for the investigation of multi‐malware attack in wireless sensor network

  • Shashank Awasthi,
  • Pramod Kumar Srivastava,
  • Naresh Kumar,
  • Rudra Pratap Ojha,
  • Purnendu Shekhar Pandey,
  • Rajesh Singh,
  • Anita Gehlot,
  • Neeraj Priyadarshi,
  • Rituraj Jain,
  • Yohannes Bekuma Bakare

DOI
https://doi.org/10.1049/cmu2.12622
Journal volume & issue
Vol. 17, no. 11
pp. 1274 – 1287

Abstract

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Abstract The protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE1E2IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R0) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

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