Neural Network-based Functional Degradation for Cyber-Physical Systems

From gimmicky IoT devices to self-driving cars, cyber-physical systems have become increasingly accessible to the masses. As these systems interact intimately with the physical world, failures in the systems can lead to severe, potentially life-threatening damage. Classical cyber-physical systems, such as aircraft flight control, employ dedicated redundancies for fault tolerance. However, a more flexible and cost-effective approach to redundancy is needed as cyber-physical systems become more versatile and expand to the consumer market. In this work, we explore the synthesis of redundancies for program functionalities with neural network models. The models are trained with the data from normal program executions and will be deployed to supplant the original program functionalities when failures occur. We have tested the prototype on representative cyber-physical systems, including ArduPilot and OpenPilot. The evaluation results indicate that the approach can synthesize redundancy models for both numerical and logical programs. We also demonstrate that the redundancy models can effectively avoid bugs and security vulnerabilities in the original programs.