Abstract: Embodiments described herein provide a supervisor for fault management at a production system. During operation, the supervisor can obtain a set of sensor readings and a state of the production system. A respective sensor reading is an output of a sensor in the production system. The supervisor can then determine, using an artificial intelligence (AI) model, whether the set of sensor readings accommodates a fault associated with a corresponding sensor. Subsequently, the supervisor can determine an action that mitigates an effect of the fault and modify the set of sensor readings based on the action. Here, the modified set of sensor readings is used by a controller that controls the production system.

Abstract: Embodiments described herein provide a design architecture for co-designing a controller and a watermarking signal for a cyber-physical system. During operation, the architecture can determine, in conjunction with each other, respective values of a first set of parameters indicating operations of the controller and a second set of parameters representing the watermarking signal. Here, the watermarking signal is combinable with a control signal from the controller for monitoring an output signal of the cyber-physical system for detecting malicious data at different time instances. Subsequently, the architecture can determine a state manager for determining the states of the cyber-physical system from the monitored output signal based on the first and second sets of parameters. The architecture can also determine a detector capable of identifying presence of an attack from the states of the cyber-physical system at a plurality of time instances using the watermarking signal.

Abstract: Embodiments described herein provide a supervisor for fault management at a production system. During operation, the supervisor can obtain a set of sensor readings and a state of the production system. A respective sensor reading is an output of a sensor in the production system. The supervisor can then determine, using an artificial intelligence (AI) model, whether the set of sensor readings accommodates a fault associated with a corresponding sensor. Subsequently, the supervisor can determine an action that mitigates an effect of the fault and modify the set of sensor readings based on the action. Here, the modified set of sensor readings is used by a controller that controls the production system.

Abstract: Systems and methods receive a first indication that an Internet of Things (IoT) device is attempting to access a home network; determine that the IoT device is a trusted device; store an identifier associated with the IoT device to a blockchain in response to determining that the IoT device is a trusted device; receive a second indication that an event has occurred with respect to the IoT device; determine whether the event is a major event; and verify the identifier associated with the IoT device by storing an identity and information associated with the event to the blockchain in response to determining that the event is a major event.

Abstract: Systems and methods receive a first indication that an Internet of Things (IoT) device is attempting to access a home network; determine that the IoT device is a trusted device; store an identifier associated with the IoT device to a blockchain in response to determining that the IoT device is a trusted device; receive a second indication that an event has occurred with respect to the IoT device; determine whether the event is a major event; and verify the identifier associated with the IoT device by storing an identity and information associated with the event to the blockchain in response to determining that the event is a major event.