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Wireless sensor networks (WSNs) play a crucial role in data collection, but they are vulnerable to malware threats. Our research addresses this challenge through an innovative approach. We introduce a specialized model, Susceptible (S)- Infected ( \(I_1\) )- Infected-Mutant ( \(I_2\) )- Traced (T)- Recovered (R), designed to comprehensively analyze malware in WSNs and enhance their security. We introduce an Infected-Mutant state and Traced nodes to quickly find and isolate infected parts. We use Caputo fractional differential equations to study how malware spreads and find ways to control it. We find the reproduction number, and its stability is discussed. The comparative study is discussed. Numerical simulations are used to validate the memory effect in the proposed model.

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Mitigating Malware Threats in Wireless Sensor Networks: A Fractional Approach with Infected Mutant and Traced Nodes

  • 会议论文

  • 发表日期：

    2025年

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  • Abilasha Balakumar,
  • Sumathi Muthukumar,
  • Veeramani Chinnadurai

摘要

Wireless sensor networks (WSNs) play a crucial role in data collection, but they are vulnerable to malware threats. Our research addresses this challenge through an innovative approach. We introduce a specialized model, Susceptible (S)- Infected ( \(I_1\) )- Infected-Mutant ( \(I_2\) )- Traced (T)- Recovered (R), designed to comprehensively analyze malware in WSNs and enhance their security. We introduce an Infected-Mutant state and Traced nodes to quickly find and isolate infected parts. We use Caputo fractional differential equations to study how malware spreads and find ways to control it. We find the reproduction number, and its stability is discussed. The comparative study is discussed. Numerical simulations are used to validate the memory effect in the proposed model.