A secure architecture for IoT with supply chain risk management

We proposes the development of a cyber-secure, Internet of Things (IoT), supply chain risk management architecture. The proposed architecture is designed to reduce vulnerabilities of malicious supply chain risks by applying machine learning (ML), cryptographic hardware monitoring (CHM), and distributed system coordination (DSC) techniques to mitigate the consequences of unforeseen (including general component failure) threats. In combination, these crosscutting technologies will be integrated into Instrumentation-and-Control/Operator-in-the-Loop (ICOL) architecture to learn normal and abnormal system behaviors. The detection of absolute or perceived abnormal system-component behaviors will trigger an ICOL alert that will require an operator's manual verification-response action (i.e., that the detected alert is, or is not a viable control system threat). The operator's verification-response will be fed back into the ML systems to recalibrate the perceived normal or abnormal state of the system's behavior.