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2018 | OriginalPaper | Buchkapitel

SILK-TV: Secret Information Leakage from Keystroke Timing Videos

verfasst von : Kiran S. Balagani, Mauro Conti, Paolo Gasti, Martin Georgiev, Tristan Gurtler, Daniele Lain, Charissa Miller, Kendall Molas, Nikita Samarin, Eugen Saraci, Gene Tsudik, Lynn Wu

Erschienen in: Computer Security

Verlag: Springer International Publishing

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Abstract

Shoulder surfing attacks are an unfortunate consequence of entering passwords or PINs into computers, smartphones, PoS terminals, and ATMs. Such attacks generally involve observing the victim’s input device. This paper studies leakage of user secrets (passwords and PINs) based on observations of output devices (screens or projectors) that provide “helpful” feedback to users in the form of masking characters, each corresponding to a keystroke. To this end, we developed a new attack called Secret Information Leakage from Keystroke Timing Videos (SILK-TV). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM or PoS. We conducted several studies in various envisaged attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, SILK-TV recovers 8-character alphanumeric passwords in as little as 19 attempts. However, when guessing PINs, SILK-TV yields no substantial speedup compared to brute force. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper.

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Fußnoten
1
Where required, IRB approvals were duly obtained prior to the experiments.
 
2
See for example the lists maintained by https://​haveibeenpwned.​com/​.
 
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Metadaten
Titel
SILK-TV: Secret Information Leakage from Keystroke Timing Videos
verfasst von
Kiran S. Balagani
Mauro Conti
Paolo Gasti
Martin Georgiev
Tristan Gurtler
Daniele Lain
Charissa Miller
Kendall Molas
Nikita Samarin
Eugen Saraci
Gene Tsudik
Lynn Wu
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-99073-6_13