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Addressing the Effects of Temperature Variations on Intrinsic Memory-Based Physical Unclonable Functions
... Physically unclonable function (PUF) is the technique of digital fingerprint on physical semiconductor devices. This fingerprint remains almost unaffected due to any temperature, humidity, or stability variations [18,19,20]. There has been research that investigates PUF from volatile memories which can be found in [21,22,23,24,25,26,27,28] and non-volatile memories in [29]. ...
Embedded systems or micro controller based modules have become increasingly prevalent in our daily lives. However, the security of embedded devices as well as the authenticity of hardware has become an increasing concern within the growing Internet of Things (IoT) space. In this paper we setup an experiment environment where SLC flash program disturbance is observed. We discovered that intra-page disturbance is easier to be produced than inter-page disturbance. We also observed that adjacent pages are paired in (2n, 2n+1) manner, and disturbance only occurs within a pair. Lastly, we found that as page number increases from 0 to 63, it becomes more difficult to observe the first bit flip within a page, and thus more difficult to achieve the disturbance stable state.
... In [21], DRAM-based PUFs, namely retentionbased and row hammer PUFs, are evaluated in the context of temperature and voltage variations, and are shown to be highly dependent on temperature. The effect of temperature variation on PUFs is further investigated in [22], and a method is proposed to account for the negative impacts. Specifically, temperature sensors, which are often present in DRAM modules, are used with a DRAM-based PUF in order to create an authentication protocol suitable for varying temperatures that restricts access to PUF responses when it's determined that the operation could be compromised by the environment's temperature. ...
In today's digital age, the ease of data collection, transfer, and storage continue to shape modern society and the ways we interact with our world. The advantages are numerous, but there is also an increased risk of information unintentionally falling into the wrong hands. Finding methods of protecting sensitive information at the hardware level is of utmost importance, and in this paper, we aim to provide a survey on recent developments in attacks on lightweight hardware-based security primitives (LHSPs) designed to do just that. Specifically, we provide an analysis of the attack resilience of these proposed LHSPs in an attempt to bring awareness to any vulnerabilities that may exist. We do this in the hope that it will encourage the continued development of attack countermeasures as well as completely new methods of data protection in order to prevent the discussed methods of attack from remaining viable in the future. The types of LHSPs discussed include physical unclonable functions (PUFs) and true random number generators (TRNGs), with a primary emphasis placed on PUFs.
... Generally, converting analog measurement DRAM startup values to binary strings is also known in the field of biometrics. The proposed approach, several parameters affected by temperature [18] and measurement noise is used to trade off reliability and uniqueness key bits. Our approach for quantizing features to two bits is most clearly illustrated in Figure 1. ...
Security primitives based on Dynamic Random Access Memory (DRAM) can provide cost-efficient and practical security solutions, especially for resource-constrained devices, such as hardware used in the Internet of Things (IoT), as DRAMs are an intrinsic part of most contemporary computer systems [1]. Over the past few years, DRAM-based physical unclonable functions became very popular among researchers in this field. However, similar to other types of PUFs, DRAM PUF reliability for authentication and key generation is highly dependent on its resistance against the environmental noises such as Temperature variation, Voltage variations, and Device aging. This paper addresses the challenges related to the reliability and robustness of DRAM PUFs under noisy environments. In this paper we apply a new approach (Quantization) that extracts keys from DRAM startup values with a high reliability and stability rate. This quantization technique identifies suitable features from power-up values of DRAM memories and quantize them into binary bits using tunable parameters that control and predict the environmental noises. Our experimental result shows a high reliability and min-entropy rate for relatively large number of DRAM key bits.
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