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