Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security 2016
DOI: 10.1145/2976749.2978300
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My Smartphone Knows What You Print

Abstract: Additive manufacturing, also known as 3D printing, has been increasingly applied to fabricate highly intellectual property (IP) sensitive products. However, the related IP protection issues in 3D printers are still largely underexplored. On the other hand, smartphones are equipped with rich onboard sensors and have been applied to pervasive mobile surveillance in many applications. These facts raise one critical question: is it possible that smartphones access the sidechannel signals of 3D printer and then hac… Show more

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Cited by 76 publications
(6 citation statements)
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References 21 publications
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“…Song et al [54] proposed an attacking strategy that is similar to Faruque et al [21] in terms of acoustic emission. But there are several differences between those works.…”
Section: Attack Methods On Ammentioning
confidence: 99%
See 2 more Smart Citations
“…Song et al [54] proposed an attacking strategy that is similar to Faruque et al [21] in terms of acoustic emission. But there are several differences between those works.…”
Section: Attack Methods On Ammentioning
confidence: 99%
“…Firstly, in [21], they used a professional audio recorder to record the audio. But, in [54], the authors used sensors available on smartphones. Secondly, instead of using a single side channel, the of [54] used magnetic and acoustic emanations.…”
Section: Attack Methods On Ammentioning
confidence: 99%
See 1 more Smart Citation
“…Existem algumas vantagens no uso da impressão 3D em comparação aos processos de fabricação tradicionais, entre eles: (i) eficiência -produção rápida e econômica, com baixas quantidades de material residual; (ii) criatividade -ideal para confecção de geometrias complexas; e (iii) acessibilidade -preço razoável de máquinas e materiais (Song et al, 2016).…”
Section: Referencial Teóricounclassified
“…Furthermore, attacks on 3D printing files and networks have been demonstrated which capture/steal model information and inject subtle, malicious changes to the data to cause printer malfunctions or product failures [ 27 , 28 , 29 ]. Other attacks focus on using machine learning and other methods to reconstruct GCode by gathering sensor data and monitoring the physical properties of the printer [ 30 ]. Lastly, the CAN bus has previously been exploited in commercial 3D printers to modify target values and feedback loops through replay attacks and impersonation.…”
Section: Introductionmentioning
confidence: 99%