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Physical unclonable functions (PUFs) are hardware-oriented primitives thatexploit manufacturing variations to generate a unique identity for a physicalsystem. Recent advancements showed how DRAM can be exploited to implement PUFs.DRAM PUFs require no additional circuits for PUF operations and can be used inmost of the applications with resource-constrained nodes such as Internet ofThings (IoT) networks. However, the existing DRAM PUF solutions either requireto interrupt other functions in the host system, or provide unreliableresponses due to their sensitiveness to the environmental conditions.  In this paper, we propose EPUF, a novel strategy to extract random and uniquefeatures from DRAM cells to generate reliable PUF responses. In particular, weuse the bitmap images of the binary DRAM values and their entropy features. Weshow via real device experiments that EPUF is approximately $1.7$ times fasterthan other state of the art solutions, achieves $100\%$ reliability, generatesfeatures with $47.79\%$ uniqueness, and supports a large set of CRP that leadsto new potentials for DRAM PUF-based authentication. We also propose alightweight authentication protocol based on EPUF, which not only provides farbetter security guarantees but also outperforms the state-of-the-art in termsof communication overhead and computational cost.

EPUF: A Novel Scheme Based on Entropy Features of Latency-based DRAM PUFs Providing Lightweight Authentication in IoT Networks