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Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach

Author

Listed:
  • Roberto Casado-Vara

    (Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Matemáticas y Computación, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006 Burgos, Spain)

  • Marcos Severt

    (Department of Computer Sciences, Universidad de Salamanca, 37007 Salamanca, Spain)

  • Antonio Díaz-Longueira

    (Department of Industrial Engineering, CTC, CITIC, University of A Coruña, Rúa Mendizábal, s/n, 15403 Ferrol, Spain)

  • Ángel Martín del Rey

    (Department of Applied Mathematics, Universidad de Salamanca, 37007 Salamanca, Spain)

  • Jose Luis Calvo-Rolle

    (Department of Industrial Engineering, CTC, CITIC, University of A Coruña, Rúa Mendizábal, s/n, 15403 Ferrol, Spain)

Abstract

With the progress and evolution of the IoT, which has resulted in a rise in both the number of devices and their applications, there is a growing number of malware attacks with higher complexity. Countering the spread of malware in IoT networks is a vital aspect of cybersecurity, where mathematical modeling has proven to be a potent tool. In this study, we suggest an approach to enhance IoT security by installing security updates on IoT nodes. The proposed method employs a physically informed neural network to estimate parameters related to malware propagation. A numerical case study is conducted to evaluate the effectiveness of the mitigation strategy, and novel metrics are presented to test its efficacy. The findings suggest that the mitigation tactic involving the selection of nodes based on network characteristics is more effective than random node selection.

Suggested Citation

  • Roberto Casado-Vara & Marcos Severt & Antonio Díaz-Longueira & Ángel Martín del Rey & Jose Luis Calvo-Rolle, 2024. "Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach," Mathematics, MDPI, vol. 12(2), pages 1-24, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:250-:d:1317996
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