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Snoopy : sniffing your smartwatch passwords via deep sequence learning
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Xiaoxuan Lu, Chris, Bowen, Du, Wen, Hongkai, Wang, Sen, Markham, Andrew, Martinovic, Ivan, Shen, Yiran and Trigoni, Niki (2017) Snoopy : sniffing your smartwatch passwords via deep sequence learning. In: Ubicomp ’18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Singapore, 8-12 Oct 2018. Published in: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 1 (4). p. 152. doi:10.1145/3161196
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Official URL: http://doi.org/10.1145/3161196
Abstract
Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile platforms. One can access online banking or even make payments on a smartwatch without a paired phone. This makes smartwatches more attractive and vulnerable to malicious attacks, which to date have been largely overlooked. In this paper, we demonstrate Snoopy, a password extraction and inference system which is able to accurately infer passwords entered on Android/Apple watches within 20 attempts, just by eavesdropping on motion sensors. Snoopy uses a uniform framework to extract the segments of motion data when passwords are entered, and uses novel deep neural networks to infer the actual passwords. We evaluate the proposed Snoopy system in the real-world with data from 362 participants and show that our system offers a ~ 3-fold improvement in the accuracy of inferring passwords compared to the state-of-the-art, without consuming excessive energy or computational resources. We also show that Snoopy is very resilient to user and device heterogeneity: it can be trained on crowd-sourced motion data (e.g. via Amazon Mechanical Turk), and then used to attack passwords from a new user, even if they are wearing a different model.
This paper shows that, in the wrong hands, Snoopy can potentially cause serious leaks of sensitive information. By raising awareness, we invite the community and manufacturers to revisit the risks of continuous motion sensing on smart wearable devices.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | H Social Sciences > HF Commerce Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Smartwatches , Ubiquitous computing, Human-computer interaction, User-centered system design, Smartwatches--Computer networks--Security measures, Data protection--Security measures., Smartwatches--Access control, Computers--Access control--Passwords | ||||||
Journal or Publication Title: | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) | ||||||
Publisher: | ACM | ||||||
Book Title: | ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) | ||||||
Official Date: | 31 December 2017 | ||||||
Dates: |
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Volume: | 1 | ||||||
Number: | 4 | ||||||
Page Range: | p. 152 | ||||||
DOI: | 10.1145/3161196 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 1 November 2017 | ||||||
Date of first compliant Open Access: | 12 February 2018 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | Ubicomp ’18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Singapore | ||||||
Date(s) of Event: | 8-12 Oct 2018 | ||||||
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