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<p>The Internet of Things (IoT) has been revolutionizing the agricultural industry by providing farmers with unprecedented opportunities to monitor and control their crops, livestock, and farm equipment in real-time, which is named as IoT based Agriculture (Ag-IoT). Ag-IoT relies on the use of communication technology, internet, and other wireless technologies which makes it prone to various cyber attack and cyber crimes. To address the growing security and forensic challenges in Ag-IoT, we propose a Digital Forensics and Incident Response Management Model (DFIRMM). The proposed model focuses on the identification, analysis, and mitigation of security incidents, along with support for the investigation of digital forensics tailored to the unique requirements of Ag-IoT. The proposed model is validated through a case study on MQTT enabled smart agriculture network with machine learning based analysis. We believe this proposed model will redefine how security incidents are handled in smart agriculture industries and impact their growth.</p>

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Digital forensics and incident response management model for IoT based agriculture

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月22日

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  • Santoshi Rudrakar,
  • Parag Rughani,
  • Lakshminarayana Sadineni

摘要

The Internet of Things (IoT) has been revolutionizing the agricultural industry by providing farmers with unprecedented opportunities to monitor and control their crops, livestock, and farm equipment in real-time, which is named as IoT based Agriculture (Ag-IoT). Ag-IoT relies on the use of communication technology, internet, and other wireless technologies which makes it prone to various cyber attack and cyber crimes. To address the growing security and forensic challenges in Ag-IoT, we propose a Digital Forensics and Incident Response Management Model (DFIRMM). The proposed model focuses on the identification, analysis, and mitigation of security incidents, along with support for the investigation of digital forensics tailored to the unique requirements of Ag-IoT. The proposed model is validated through a case study on MQTT enabled smart agriculture network with machine learning based analysis. We believe this proposed model will redefine how security incidents are handled in smart agriculture industries and impact their growth.