Given the ubiquity of memory in commodity electronic devices, fingerprinting memory is a compelling proposition, especially for low-end Internet of Things (IoT) devices where cryptographic modules are often unavailable. However, the use of fingerprints in security functions is challenged by the inexact reproductions of fingerprints from the same device at different time instances due to various noise sources causing, small, but unpredictable variations in fingerprint measurements. Our study formulates a novel and \textit{pragmatic} approach to achieve the elusive goal of affording highly reliable fingerprints from device memories. We investigate the transformation of raw fingerprints into a noise-tolerant space where the generation of fingerprints from memory biometrics is intrinsically highly reliable. Further, we derive formal performance bounds to support practitioners to adopt our methods for practical applications. Subsequently, we demonstrate the expressive power of our formalization by using it to investigate the practicability of extracting noise-tolerant fingerprints from commodity devices. We have employed a set of 38 memory chips including SRAM (69,206,016 cells), Flash (3,902,976 cells) and EEPROM (32,768 cells) ubiquitously embedded in low-end commodity devices from 6 different manufacturers for extensive experimental validations. Our results demonstrate that noise-tolerant fingerprints -- achieving a key failure rate less than $10^{-6}$ -- can always be efficiently afforded from tested memories with a solely fingerprint snap-shot enrollment. Further, we employ a low-cost wearable Bluetooth inertial sensor and demonstrate a practical, end-to-end implementation of a remote attestation security function built upon a root key from noise-tolerant SRAM fingerprints generated on demand and at run-time.

翻译：鉴于商品电子设备的记忆普遍存在,指纹记忆是一个令人信服的建议,特别是对于低端的Things(IoT)互联网设备而言,往往没有加密模块;然而,由于不同时间不同,由于各种噪音源造成、规模较小但无法预测的指纹测量差异,在不同的场合,同一设备的指纹复制不精确,因此在安全功能中使用指纹受到挑战。我们的研究提出了一种新颖和Textit{propmatic}的方法,以实现从设备记忆中提供高度可靠的指纹这一难以实现的目标。我们调查了原始指纹转换成一个噪音耐控空间的情况,在那里,从记忆的指纹中生成的指纹本质上是高度可靠的。此外,我们获得正式的性性功能是支持从不同时候从同一装置中提取不精确的指纹,因为各种噪音来源造成,但是在指纹测量方面的变化变化不定。我们使用一套38个记忆芯片,包括SRAM(69,206,016个细胞)、闪存(3,902,976个细胞)和EPROM(32,768个),从记忆中生成的指纹,从记忆中生成的指纹是完全可靠的指纹。 我们的内存的内存的关键-78-时间定位系统运行记录运行记录运行中,可以显示一个不那么久,一个不固定,一个不固定的硬化的机能,一个运行,一个在10。