Publication

DRAM-based Physically Unclonable Functions and the Need for Proper Evaluation

Pascal Ahr, Christoph Lipps, Hans Dieter Schotten

In: Proceeding of the European Conference on Cyber Warfare and Security. European Conference on Cyber Warfare and Security (ECCWS-2022) June 16-17 Chester United Kingdom ACI 2022.

Abstract

Dynamic Random-Access Memory (DRAM)-based Physically Unclonable Functions (PUFs) are a part of the Physical Layer Security (PhySec) domain. Those electrical PUFs are memory based and exhibit a high availability, Shannon Entropy, low energy consumption and high amount of Challenge Response Pairs (CRPs). Because of those properties, the DRAM PUF is a promising approach for security applications in the Industrial Internet of Things (IIoT) context as well as securing the Sixth-Generation (6G) Wireless Systems and edge computing. DRAM, with its most common one-Transistor one-Capacitor (1T1C) architecture, and as a volatile memory is embedded in almost every modern computing unit. Regarding the PUF security applications, four main types of applications are currently distinguished in the scientific community: Retention Error, Row Hammer, Startup and Latency PUFs. Thereby these differ in their procedure in how responses are generated as well as by the physical mechanisms. Each of them with varying properties in terms of availability, reliability, uniqueness and uniformity. To examine this, and to obtain comparable results, this work proposes to compare the four different DRAM-PUF types i) with the same metrics of evaluation and ii) implemented on the same DRAM cells. This represents both the difference with regard to the work done in the literature and the added value of this work presented. As far as known, there is no work to date that performs the intended evaluations using the same evaluation platform under the identical conditions. However, this is required for comparable results. This consistent comparison is ensured by a self-developed and implemented evaluation platform, which is accordingly equipped with a significant number of DRAMs. By an appropriate high volume of measurements, a corresponding resolution will be given. Monitoring the environmental conditions prevents from wrong interpretations caused by environmental influences but also provides useful context information. Furthermore, a detailed technical and physical background will be described. The results of this approach will assist by the consideration of which DRAM-PUF is appropriate in which (environmental) conditions and thereby provide a guideline for practitioners.

Projekte

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz