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The widespread adoption of networked devices, sophisticated automation and data-driven processes in industry – also known as Industry 4.0 – has boosted the quantity and quality of manufacturing products. However, the benefits come with substantial increases in the attack surfaces of manufacturing processes and systems. In addition to affecting the quality and readiness of critical products, attacks against manufacturing processes and systems can have severe physical consequences, including human injury and death. This chapter presents the results of remote network-based control injection attacks on a computer numerical control mill. The focus is on the impacts of attacks during computer numerical control mill operation. A machine-agnostic, affordable and scalable solution for attack monitoring is developed by considering the physical effects of the attacks on wax workpieces. A simple threshold-based method for detecting attacks is demonstrated and its effectiveness is evaluated.

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Detecting Control Injection Attacks Using Energy Data Anomalies in Computer Numerical Control Machining

  • 会议论文

  • 发表日期：

    2025年

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  • Jeffrey Kimmell,
  • Vladimir Orlyanchik,
  • Logan Sturm,
  • Joel Dawson,
  • Curtis Taylor

摘要

The widespread adoption of networked devices, sophisticated automation and data-driven processes in industry – also known as Industry 4.0 – has boosted the quantity and quality of manufacturing products. However, the benefits come with substantial increases in the attack surfaces of manufacturing processes and systems. In addition to affecting the quality and readiness of critical products, attacks against manufacturing processes and systems can have severe physical consequences, including human injury and death. This chapter presents the results of remote network-based control injection attacks on a computer numerical control mill. The focus is on the impacts of attacks during computer numerical control mill operation. A machine-agnostic, affordable and scalable solution for attack monitoring is developed by considering the physical effects of the attacks on wax workpieces. A simple threshold-based method for detecting attacks is demonstrated and its effectiveness is evaluated.