"How Digital Twins Could Protect Manufacturers From Cyberattacks"

Digital twins, which are detailed virtual copies of physical objects, are paving the way for better products in healthcare, aerospace, and other industries. A new study suggests that cybersecurity may also fit perfectly into the digital twin portfolio. As robots and other production equipment become increasingly remotely accessible, new cyberattack entry points are introduced. Therefore, a team of researchers from the National Institute of Standards and Technology (NIST) and the University of Michigan developed a cybersecurity framework that combines digital twin technology with Machine Learning (ML) and human skills to flag cyberattack indicators. NIST and the University of Michigan demonstrated the feasibility of their technique in a paper published in IEEE Transactions on Automation Science and Engineering by detecting cyberattacks directed at a 3D printer in their lab. In addition, they emphasize that the framework is applicable to various manufacturing technologies. This article continues to discuss the new strategy for detecting cyberattacks on manufacturing systems, which involves using Artificial Intelligence (AI) to monitor a digital twin and is fed real-time data from the physical system.

NIST reports "How Digital Twins Could Protect Manufacturers From Cyberattacks"