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The COVID-19 pandemic has been accompanied by a hidden pandemic of malicious software (malware) targeting connected devices, which is growing in sophistication, volume and impact. Characterising the dynamics of this pandemic of device malware is a fast-evolving area of research to which machine learning (ML) is being applied, increasingly in combination with multidisciplinary models taken from fields such as languages, epidemiology, biology, biostatistics and health. We reflect on the current state of nature-inspired research approaches and on future directions that show promise for better understanding the hidden pandemic affecting devices in the Internet of Things (IoT).

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Unveiling the Hidden Pandemic of IoT Malware with Biological and Health Approaches

  • 会议论文

  • 发表日期：

    2025年

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  • Jasmin Craufurd-Hill,
  • Benjamin Kaehler,
  • Kathryn Kasmarik,
  • Timothy Lynar

摘要

The COVID-19 pandemic has been accompanied by a hidden pandemic of malicious software (malware) targeting connected devices, which is growing in sophistication, volume and impact. Characterising the dynamics of this pandemic of device malware is a fast-evolving area of research to which machine learning (ML) is being applied, increasingly in combination with multidisciplinary models taken from fields such as languages, epidemiology, biology, biostatistics and health. We reflect on the current state of nature-inspired research approaches and on future directions that show promise for better understanding the hidden pandemic affecting devices in the Internet of Things (IoT).