ScatterVerif: Verification of Electronic Boards Using Reflection Response of Power Distribution Network

IF 2.1 4区 计算机科学 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE ACM Journal on Emerging Technologies in Computing Systems Pub Date : 2022-10-13 DOI:https://dl.acm.org/doi/10.1145/3513087
Tahoura Mosavirik, Fatemeh Ganji, Patrick Schaumont, Shahin Tajik
{"title":"ScatterVerif: Verification of Electronic Boards Using Reflection Response of Power Distribution Network","authors":"Tahoura Mosavirik, Fatemeh Ganji, Patrick Schaumont, Shahin Tajik","doi":"https://dl.acm.org/doi/10.1145/3513087","DOIUrl":null,"url":null,"abstract":"<p>The globalization of electronic systems’ fabrication has made some of our most critical systems vulnerable to supply chain attacks. Implanting spy chips on the <b>printed circuit boards (PCBs)</b> or replacing genuine components with counterfeit/recycled ones are examples of such attacks. Unfortunately, conventional attack detection schemes for PCBs are ad hoc, costly, unscalable, and error prone. This work introduces a holistic physical verification framework for PCBs, called <i>ScatterVerif</i>, based on the characterization of the PCBs’ power distribution network. First, we demonstrate how scattering parameters, frequently used for impedance characterization of RF circuits, can characterize the entire PCB with a single measurement. Second, we present how a class of machine learning algorithms, namely the Gaussian mixture model, can be applied to the measurements to automatically classify/cluster the genuine and tampered/counterfeit PCBs. We show that these attacks affect the overall impedance of a PCB differently in various frequency ranges, hence the conventional impedance measurements using a constant-frequency electrical stimulus might leave the attack undetected. We conduct extensive experiments on counterfeit and tampered devices and demonstrate that these attacks can be detected with high confidence. Finally, we show that the acquired data from the power distribution network characterization can also be deployed for fingerprinting genuine PCBs.</p>","PeriodicalId":50924,"journal":{"name":"ACM Journal on Emerging Technologies in Computing Systems","volume":"2 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Journal on Emerging Technologies in Computing Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/https://dl.acm.org/doi/10.1145/3513087","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0

Abstract

The globalization of electronic systems’ fabrication has made some of our most critical systems vulnerable to supply chain attacks. Implanting spy chips on the printed circuit boards (PCBs) or replacing genuine components with counterfeit/recycled ones are examples of such attacks. Unfortunately, conventional attack detection schemes for PCBs are ad hoc, costly, unscalable, and error prone. This work introduces a holistic physical verification framework for PCBs, called ScatterVerif, based on the characterization of the PCBs’ power distribution network. First, we demonstrate how scattering parameters, frequently used for impedance characterization of RF circuits, can characterize the entire PCB with a single measurement. Second, we present how a class of machine learning algorithms, namely the Gaussian mixture model, can be applied to the measurements to automatically classify/cluster the genuine and tampered/counterfeit PCBs. We show that these attacks affect the overall impedance of a PCB differently in various frequency ranges, hence the conventional impedance measurements using a constant-frequency electrical stimulus might leave the attack undetected. We conduct extensive experiments on counterfeit and tampered devices and demonstrate that these attacks can be detected with high confidence. Finally, we show that the acquired data from the power distribution network characterization can also be deployed for fingerprinting genuine PCBs.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
ScatterVerif:利用配电网反射响应对电路板进行验证
电子系统制造的全球化使得我们的一些最关键的系统容易受到供应链攻击。在印刷电路板(pcb)上植入间谍芯片或用假冒/回收的组件替换正品组件都是此类攻击的例子。不幸的是,传统的pcb攻击检测方案是临时的、昂贵的、不可扩展的,而且容易出错。这项工作介绍了pcb的整体物理验证框架,称为ScatterVerif,基于pcb配电网络的特征。首先,我们展示了经常用于射频电路阻抗表征的散射参数如何通过一次测量来表征整个PCB。其次,我们介绍了一类机器学习算法,即高斯混合模型,如何应用于测量,以自动分类/聚类正品和篡改/假冒pcb。我们表明,这些攻击在不同频率范围内对PCB的整体阻抗影响不同,因此使用恒频电刺激的传统阻抗测量可能会使攻击未被检测到。我们对伪造和篡改的设备进行了广泛的实验,并证明可以高可信度地检测到这些攻击。最后,我们证明了从配电网络特性中获得的数据也可以用于指纹识别正品pcb。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
ACM Journal on Emerging Technologies in Computing Systems
ACM Journal on Emerging Technologies in Computing Systems 工程技术-工程:电子与电气
CiteScore
4.80
自引率
4.50%
发文量
86
审稿时长
3 months
期刊介绍: The Journal of Emerging Technologies in Computing Systems invites submissions of original technical papers describing research and development in emerging technologies in computing systems. Major economic and technical challenges are expected to impede the continued scaling of semiconductor devices. This has resulted in the search for alternate mechanical, biological/biochemical, nanoscale electronic, asynchronous and quantum computing and sensor technologies. As the underlying nanotechnologies continue to evolve in the labs of chemists, physicists, and biologists, it has become imperative for computer scientists and engineers to translate the potential of the basic building blocks (analogous to the transistor) emerging from these labs into information systems. Their design will face multiple challenges ranging from the inherent (un)reliability due to the self-assembly nature of the fabrication processes for nanotechnologies, from the complexity due to the sheer volume of nanodevices that will have to be integrated for complex functionality, and from the need to integrate these new nanotechnologies with silicon devices in the same system. The journal provides comprehensive coverage of innovative work in the specification, design analysis, simulation, verification, testing, and evaluation of computing systems constructed out of emerging technologies and advanced semiconductors
期刊最新文献
PUF-Based Digital Money with Propagation-of-Provenance and Offline Transfers Between Two Parties SAT-based Exact Modulo Scheduling Mapping for Resource-Constrained CGRAs Towards practical superconducting accelerators for machine learning using U-SFQ Towards Energy-Efficient Spiking Neural Networks: A Robust Hybrid CMOS-Memristive Accelerator An Analysis of Various Design Pathways Towards Multi-Terabit Photonic On-Interposer Interconnects
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1