基于对称性分析的无监督混叠FPGA检测
Unsupervised Recycled FPGA Detection Using Symmetry Analysis
摘要：
最近，由于半导体供应链的激增，回收的现场可编程门阵列（FPGA）造成了显著的硬件安全问题。基于环形振荡器（Ring Oscillator，RO）的频率分析技术是目前比较流行的方法之一，但大多数研究都是使用新的FPGA（KFF）进行基于机器学习的检测，这是一种不现实的方法。在本文中，我们提出了一种新的回收FPGA检测方法，通过检查RO频率的对称性信息，使用无监督的异常检测方法。由于FPGA的对称阵列结构，FPGA上的一些相邻逻辑块具有可比较的RO频率，因此我们的方法简单地分析这些块的RO频率以确定它们的相似程度。所提出的方法有效地分类回收FPGA利用直接密度比估计通过离群值检测。使用Xilinx Artix-7 FPGA的实验表明，所提出的方法准确地分类回收的FPGA从10个新鲜的FPGA与传统的方法相比，x少的计算。
英文摘要：
Recently, recycled field-programmable gate arrays (FPGAs) pose a significant hardware security problem due to the proliferation of the semiconductor supply chain. Ring oscillator (RO) based frequency analyzing technique is one of the popular methods, where most studies used the known fresh FPGAs (KFFs) in machine learning-based detection, which is not a realistic approach. In this paper, we present a novel recycled FPGA detection method by examining the symmetry information of the RO frequency using unsupervised anomaly detection method. Due to the symmetrical array structure of the FPGA, some adjacent logic blocks on an FPGA have comparable RO frequencies, hence our method simply analyzes the RO frequencies of those blocks to determine how similar they are. The proposed approach efficiently categorizes recycled FPGAs by utilizing direct density ratio estimation through outliers detection. Experiments using Xilinx Artix-7 FPGAs demonstrate that the proposed method accurately classifies recycled FPGAs from 10 fresh FPGAs by x fewer computations compared with the conventional method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
