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dc.contributor.authorMaiti, Anindya
dc.contributor.authorJadliwala, Murtuza Shabbir
dc.contributor.authorHe, Jibo
dc.contributor.authorBilogrevic, Igor
dc.identifier.citationA. Maiti, M. Jadliwala, J. He and I. Bilogrevic, "Side-Channel Inference Attacks on Mobile Keypads Using Smartwatches," in IEEE Transactions on Mobile Computing, vol. 17, no. 9, pp. 2180-2194, 1 Sept. 2018en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractSmartwatches enable many novel applications and are fast gaining popularity. However, the presence of a diverse set of onboard sensors provides an additional attack surface to malicious software and services on these devices. In this paper, we investigate the feasibility of key press inference attacks on handheld numeric touchpads by using smartwatch motion sensors as a side-channel. We consider different typing scenarios, and propose multiple attack approaches to exploit the characteristics of the observed wrist movements for inferring individual key presses. Experimental evaluation using commercial off-the-shelf smartwatches and smartphones show that key press inference using smartwatch motion sensors is not only fairly accurate, but also comparable with similar attacks using smartphone motion sensors. Additionally, hand movements captured by a combination of both smartwatch and smartphone motion sensors yields better inference accuracy than either device considered individually.en_US
dc.description.sponsorshipDivision of Computer and Network Systems (CNS) of the US National Science Foundation (NSF) under award number 1523960.en_US
dc.relation.ispartofseriesIEEE Transactions on Mobile Computing;v.17:no.9
dc.subjectSide channel attacksen_US
dc.subjectKeystroke inferenceen_US
dc.titleSide-channel inference attacks on mobile keypads using smartwatchesen_US
dc.rights.holder© 2018, IEEEen_US

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