Cryptography and Security (cs.CR)

Abstract: 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.

Abstract: In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against such attacks typically involves viewing these inserted examples as outliers in the training set and using techniques from robust statistics to detect and remove them. In this work, we present a different approach to the backdoor attack problem. Specifically, we show that without structural information about the training data distribution, backdoor attacks are indistinguishable from naturally-occurring features in the data--and thus impossible to "detect" in a general sense. Then, guided by this observation, we revisit existing defenses against backdoor attacks and characterize the (often latent) assumptions they make and on which they depend. Finally, we explore an alternative perspective on backdoor attacks: one that assumes these attacks correspond to the strongest feature in the training data. Under this assumption (which we make formal) we develop a new primitive for detecting backdoor attacks. Our primitive naturally gives rise to a detection algorithm that comes with theoretical guarantees and is effective in practice.