Snoopy: Sniffing Your Smartwatch Passwords via Deep Sequence Learning



Lu, Chris Xiaoxuan, Du, Bowen, Wen, Hongkai, Wang, Sen, Markham, Andrew, Martinovic, Ivan, Shen, Yiran and Trigoni, Niki
(2018) Snoopy: Sniffing Your Smartwatch Passwords via Deep Sequence Learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1 (4).

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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: Article
Additional Information: 27 pages. Originally published at ACM UbiComp 2018. This version corrects some errors in the original version and add the pointer to released code & dataset
Uncontrolled Keywords: cs.HC, cs.HC, cs.CR, cs.LG
Depositing User: Symplectic Admin
Date Deposited: 29 May 2020 10:58
Last Modified: 03 May 2022 12:10
DOI: 10.1145/3161196
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3089157