Digital Twin Framework
Our novel Digital Twin Framework (DTF) is designed to improve resilience of critical infrastructure systems by continuously comparing the infrastructure state with automatically generated, AI/ML-based, real-time digital-twin simulation of the system.
Overview
As the level of automation in critical infrastructure increases, the ability to detect cyber intrusions becomes more crucial and extremely challenging. Recent cyber attacks demonstrate the devastating and widespread affects they can have on critical infrastructure.
DTF is designed specifically to detect and eventually prevent such attacks, with models validated against experimental data from two critical infrastructure experimental emulators – a canal lock system and an electric distribution system – exhibiting very different dynamics. The canal lock system’s digital twin uses a recurrent neural network trained from the experimental data collected via the DTF. A digital twin of the transmission system is created using a commercial real-time power systems simulator and integrated into our DTF along with the hardware, embedded controllers, and live sensor data using the Open Field Message Bus data model, and publish/subscribe communication protocols. A cyber attack is used on both systems to demonstrate the DTF’s detection capability.
Organization
- Sponsor: Lab Directed Research and Development, Oak Ridge National Laboratory
- Period: 2018-2020
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