Improving the Stability of APUF to 100% Without Extra Hardware Overhead for Enhancing the Performance of Security Authentication Protocols

With the increasing number of devices in the Internet of Things (IoT), security has become a necessary feature. Compared to traditional key encryption methods, IoT device authentication protocols based on strong Physically unclonable function (PUF) have the advantage of being difficult to leak and tamper with. In addition, the protocol can enhance the resistance of strong PUFs to machine learning (ML) attacks by encrypting private information. However, the quality of the authentication is strongly related to the stability of the generated responses. If a large cost is incurred to improve the stability of the strong PUF, it will consume the already scarce resources of the device. This article proposes a challenge screening strategy to improve the response stability of a commonly used strong PUF, arbiter PUF (APUF). First, a precise modeling method of APUF is carried out using the logistic-regression ML algorithm. Subsequently, random noise is injected into the challenges or PUF mathematical model and then calculated to estimate whether a stable response can be produced. Finally, stable challenges are used for IoT device authentication, while the threshold of the protocol is increased in parallel. Experimental verification shows that the proposed strategy can increase the stability of APUF responses to 100% at different temperatures. Furthermore, it does not consume hardware overhead at the device end, which is of particular significance for applications in resource-constrained conditions.