Detailed virtual copies of physical objects, called digital twins, are opening doors for better products across automotive, healthcare, aerospace, and other industries. According to a new study, cybersecurity may also fit neatly into the digital twin portfolio.

As more robots and other manufacturing equipment become remotely accessible, new entry points for malicious cyberattacks are created. To keep pace with the growing cyberthreat, a team of researchers at the National Institute of Standards and Technology (NIST) and the University of Michigan devised a cybersecurity framework that brings digital-twin technology together with machine learning and human expertise to flag indicators of cyberattacks.

In a paper published in IEEE Transactions on Automation Science and Engineering, NIST and University of Michigan researchers demonstrated the feasibility of their strategy by detecting cyberattacks aimed at a 3D printer in their lab. They also noted that the framework could be applied to a broad range of manufacturing technologies.

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