论文标题
硅约会
Silicon Dating
论文作者
论文摘要
为了为不断增长的旧电子电子产品提供服务，政府和行业客户都必须转向第三方经纪人，以供原始制造商短暂供应或停产。从第三方采购设备为不道德的灰色市场供应商提供了插入伪造设备的机会：失败，仿制或以其他方式不如原始产品。这增加了供应商的利润，而牺牲了客户系统的性能/可靠性。要检测到的伪造设备最具挑战性的类别是回收假货：恢复的真实设备，这些设备被重新售出。这些设备很难检测到，因为它们通常通过性能和参数测试，但由于年龄相关的磨损而过早失败。 为了应对检测回收设备前部署的挑战，我们开发了硅约会：使用静态随机访问记忆（SRAM）功率状态的低离头分类器，用于检测可回收的集成电路。硅约会目标设备没有已知的新记录或专门制造的反重建硬件。我们观察到，随着时间的流逝，在设备上运行的软件通过模拟域的更改将其独特的数据模式烙印到SRAM中。我们通过SRAM强力国家统计数据来衡量这种变化的水平和方向。与SRAM制造过程中的变化产生的高度对称的电力状态相反，我们表明嵌入式软件数据通常是高度不对称的，并且软件印记的电动状态不对称程度揭示了设备的使用。使用在几个微控制器上运行的嵌入式基准测试的经验结果，我们表明硅约会可以通过没有特定于软件的知识来鉴定具有84.1％精度的回收设备，并且通过在没有先前的设备注册或修改的情况下将软件知识纳入了92.0％的精度。
In order to service an ever-growing base of legacy electronics, both government and industry customers must turn to third-party brokers for components in short supply or discontinued by the original manufacturer. Sourcing equipment from a third party creates an opportunity for unscrupulous gray market suppliers to insert counterfeit devices: failed, knock-off, or otherwise inferior to the original product. This increases the supplier's profits at the expense of reduced performance/reliability of the customer's system. The most challenging class of counterfeit devices to detect is recycled counterfeits: recovered genuine devices which are re-sold as new. Such devices are difficult to detect because they typically pass performance and parametric tests but fail prematurely due to age-related wear. To address the challenge of detecting recycled devices pre-deployment, we develop Silicon Dating: a low-overhead classifier for detecting recycled integrated circuits using Static Random-Access Memory (SRAM) power-on states. Silicon Dating targets devices with no known-new record or purpose-built anti-recycling hardware. We observe that over time, software running on a device imprints its unique data patterns into SRAM through analog-domain changes; we measure the level and direction of this change through SRAM power-on state statistics. In contrast to highly symmetric power-on states produced by variation during SRAM fabrication, we show that embedded software data is generally highly asymmetric and that the degree of power-on state asymmetry imprinted by software reveals device use. Using empirical results from embedded benchmarks running on several microcontrollers, we show that Silicon Dating identifies recycled devices with 84.1% accuracy with no software-specific knowledge and with 92.0% accuracy by incorporating software knowledge---without prior device enrollment or modification.
