Leave Your Phone at the Door: Side Channels that Reveal Factory Floor Secrets

Avesta Hojjati, Anku Adhikari, Katarina Struckmann, Edward Chou, Thi Ngoc Tho Nguyen, Kushagra Madan, M. Winslett, Carl A. Gunter, William P. King
{"title":"Leave Your Phone at the Door: Side Channels that Reveal Factory Floor Secrets","authors":"Avesta Hojjati, Anku Adhikari, Katarina Struckmann, Edward Chou, Thi Ngoc Tho Nguyen, Kushagra Madan, M. Winslett, Carl A. Gunter, William P. King","doi":"10.1145/2976749.2978323","DOIUrl":null,"url":null,"abstract":"From pencils to commercial aircraft, every man-made object must be designed and manufactured. When it is cheaper or easier to steal a design or a manufacturing process specification than to invent one's own, the incentive for theft is present. As more and more manufacturing data comes online, incidents of such theft are increasing. In this paper, we present a side-channel attack on manufacturing equipment that reveals both the form of a product and its manufacturing process, i.e., exactly how it is made. In the attack, a human deliberately or accidentally places an attack-enabled phone close to the equipment or makes or receives a phone call on any phone nearby. The phone executing the attack records audio and, optionally, magnetometer data. We present a method of reconstructing the product's form and manufacturing process from the captured data, based on machine learning, signal processing, and human assistance. We demonstrate the attack on a 3D printer and a CNC mill, each with its own acoustic signature, and discuss the commonalities in the sensor data captured for these two different machines. We compare the quality of the data captured with a variety of smartphone models. Capturing data from the 3D printer, we reproduce the form and process information of objects previously unknown to the reconstructors. On average, our accuracy is within 1 mm in reconstructing the length of a line segment in a fabricated object's shape and within 1 degree in determining an angle in a fabricated object's shape. We conclude with recommendations for defending against these attacks.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"68","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2976749.2978323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 68

Abstract

From pencils to commercial aircraft, every man-made object must be designed and manufactured. When it is cheaper or easier to steal a design or a manufacturing process specification than to invent one's own, the incentive for theft is present. As more and more manufacturing data comes online, incidents of such theft are increasing. In this paper, we present a side-channel attack on manufacturing equipment that reveals both the form of a product and its manufacturing process, i.e., exactly how it is made. In the attack, a human deliberately or accidentally places an attack-enabled phone close to the equipment or makes or receives a phone call on any phone nearby. The phone executing the attack records audio and, optionally, magnetometer data. We present a method of reconstructing the product's form and manufacturing process from the captured data, based on machine learning, signal processing, and human assistance. We demonstrate the attack on a 3D printer and a CNC mill, each with its own acoustic signature, and discuss the commonalities in the sensor data captured for these two different machines. We compare the quality of the data captured with a variety of smartphone models. Capturing data from the 3D printer, we reproduce the form and process information of objects previously unknown to the reconstructors. On average, our accuracy is within 1 mm in reconstructing the length of a line segment in a fabricated object's shape and within 1 degree in determining an angle in a fabricated object's shape. We conclude with recommendations for defending against these attacks.
把手机放在门口:泄露工厂秘密的侧面渠道
从铅笔到商用飞机,每一件人造物品都必须经过设计和制造。当窃取设计或制造工艺规范比自己发明更便宜或更容易时,就存在了盗窃的动机。随着越来越多的制造业数据上线,此类盗窃事件也在增加。在本文中,我们提出了一种针对制造设备的侧通道攻击,该攻击揭示了产品的形式及其制造过程,即它是如何制造的。在攻击中,一个人有意或无意地将具有攻击功能的手机放在设备附近,或者在附近的任何手机上拨打或接听电话。执行攻击的手机记录音频和(可选的)磁力计数据。我们提出了一种基于机器学习、信号处理和人工辅助的方法,从捕获的数据中重构产品的形状和制造过程。我们演示了对3D打印机和数控铣床的攻击,每个都有自己的声学特征,并讨论了为这两个不同的机器捕获的传感器数据的共性。我们比较了各种智能手机型号的数据质量。从3D打印机获取数据,我们重现了以前不为重建者所知的物体的形状和过程信息。平均而言,我们的精度在1毫米以内重建线段的长度在一个制造对象的形状和1度以内确定一个制造对象的形状的角度。最后,我们提出了防御这些攻击的建议。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信