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SILK-TV: Secret Information Leakage from Keystroke Timing Videos

  • 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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11098)

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.

Notes

Acknowledgements

Kiran Balagani and Paolo Gasti were supported but the National Science Foundation under Grant No. CNS-1619023. Tristan Gurtler, Charissa Miller, Kendall Molas, and Lynn Wu were supported by the National Science Foundation under Grant No. CNS-1559652. This work is partially supported by the EU TagItSmart! Project (agreement H2020-ICT30-2015-688061), and the EU-India REACH Project (agreement ICI+/2014/342-896).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kiran S. Balagani
    • 1
  • Mauro Conti
    • 2
  • Paolo Gasti
    • 1
  • Martin Georgiev
    • 3
    • 4
  • Tristan Gurtler
    • 1
    • 5
  • Daniele Lain
    • 2
    • 6
  • Charissa Miller
    • 1
    • 7
  • Kendall Molas
    • 1
  • Nikita Samarin
    • 3
    • 8
  • Eugen Saraci
    • 2
  • Gene Tsudik
    • 3
  • Lynn Wu
    • 1
    • 9
  1. 1.New York Institute of TechnologyNew YorkUSA
  2. 2.University of PaduaPaduaItaly
  3. 3.University of CaliforniaIrvineUSA
  4. 4.University of OxfordOxfordUK
  5. 5.University of Illinois at Urbana-ChampaignChampaignUSA
  6. 6.ETH ZurichZurichSwitzerland
  7. 7.Rochester Institute of TechnologyRochesterUSA
  8. 8.University of CaliforniaBerkeleyUSA
  9. 9.Bryn Mawr CollegePhiladelphiaUSA

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