EPUF: An Entropy-Derived Latency-Based DRAM Physical Unclonable Function for Lightweight Authentication in Internet of Things

Physical Unclonable Functions (PUFs) are hardware-based mechanisms that exploit inherent manufacturing variations to generate unique identifiers for devices. Dynamic Random Access Memory (DRAM) has emerged as a promising medium for implementing PUFs, providing a cost-effective solution without the need for additional circuitry. This makes DRAM PUFs ideal for use in resource-constrained environments such as Internet of Things (IoT) networks. However, current DRAM PUF implementations often either disrupt host system functions or produce unreliable responses due to environmental sensitivity. In this paper, we present EPUF, a novel approach to extracting random and unique features from DRAM cells to generate reliable PUF responses. We leverage bitmap images of binary DRAM values and their entropy features to enhance the robustness of our PUF. Through extensive real-world experiments, we demonstrate that EPUF is approximately 1.7 times faster than existing solutions, achieves 100% reliability, produces features with 47.79% uniqueness, and supports a substantial set of Challenge-Response Pairs (CRPs). These capabilities make EPUF a powerful tool for DRAM PUF-based authentication. Based on EPUF, we then propose a lightweight authentication protocol that not only offers superior security features but also surpasses state-of-the-art authentication schemes in terms of communication overhead and computational efficiency.