  The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a
mechanism that can evaluate critical infrastructures' operational behaviour and
security without affecting the operation of live systems. In this regard,
Digital Twins (DTs) provide actionable insights through monitoring, simulating,
predicting, and optimizing the state of CPSs. Through the use cases, including
system testing and training, detecting system misconfigurations, and security
testing, DTs strengthen the security of CPSs throughout the product lifecycle.
However, such benefits of DTs depend on an assumption about data integrity and
security. Data trustworthiness becomes more critical while integrating multiple
components among different DTs owned by various stakeholders to provide an
aggregated view of the complex physical system. This article envisions a
blockchain-based DT framework as Trusted Twins for Securing Cyber-Physical
Systems (TTS-CPS). With the automotive industry as a CPS use case, we
demonstrate the viability of the TTS-CPS framework in a proof of concept. To
utilize reliable system specification data for building the process knowledge
of DTs, we ensure the trustworthiness of data-generating sources through
integrity checking mechanisms. Additionally, the safety and security rules
evaluated during simulation are stored and retrieved from the blockchain,
thereby establishing more understanding and confidence in the decisions made by
the underlying systems. Finally, we perform formal verification of the TTS-CPS.
