Abstract
The Internet of Things (IoT) plays an important role in all aspects of production and day-to-day life. However, owing to the frequently trusted authentication vulnerabilities, the physical unclonable function (PUF) has unique advantages in the field of equipment authentication because of its non-storage and non-volatility. Nevertheless, PUFs are vulnerable to machine learning (ML) attacks. Once a model is constructed accurately, the secrecy of the PUF is lost. Therefore, Bagua matrices are proposed in this study, which can greatly reduce the accuracy of modeling by encrypting the challenge information. On this basis, a whole-process configurable IoT sensing device protocol was constructed for authentication and transmission, and different matrix encryption methods were configured according to the needs of the different devices. Moreover, on the premise of trusted authentication, the perceptual information can be encrypted using a preset matrix. According to the implementation results of the scheme, the resistance to the ML attacks of the PUF improved significantly and the device authentication and encrypted transmission could operate normally.
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