Secure Dynamic PUF for IoT Security

This student research forum paper is based on our accepted work [1]. The widespread adoption of the Internet of Things (IoT) has brought many benefits to our lives. Still, the low-power, heterogeneous, and resource-constrained nature of IoT devices makes it difficult to ensure secure communication and authenticity. Physical Unclonable Functions (PUFs) provide a promising solution by generating a unique and device-specific identity through manufacturing process variations without requiring additional resources. However, recent advances in machine learning algorithms like artificial neural networks and logistic regression have made it possible to predict PUF responses by training the model. Machine learning models can use multiple challenges and responses to predict accurate results from the PUF. To address this concern, we propose integrating a dynamically configurable PUF structure into the design to counteract machine learning attacks. The dynamicity of the PUF makes it challenging for machine learning models to predict PUF responses.