TapPrints : Your Finger Taps Have Fingerprints
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Transcript of TapPrints : Your Finger Taps Have Fingerprints
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TapPrints: Your Finger Taps Have Fingerprints
Presented by: Tom Staley
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AboutPaper by
Emiliano MiluzzoAlexander VarshavskySuhrid BalakrishnanRomit Roy Choudhury
Originally presented at MobiSys2012, June 27, 2012
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IntroductionDetermining location of screen taps using
accelerometer and gyroscopesCould lead to attackers using this info to
track inputs“TapPrints- a framework for inferring
location of taps on mobile devices”
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Current State of SensorsMobile sensors becoming more powerfulMany types of data: patient monitoring,
localization, context-awareness, etc.Rumored that insurance companies are
trying to use dietary patterns to determine cost and coverage of policies
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Using Gyroscopes
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TapPrintsImplemented on Google Nexus S, Apple
iPhone 4, Samsung Galaxy Tab 10.1Over 40,000 taps collected from 10 users
over 4 weeks80-90% accuracy, enough to guess a
password
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How Data Could be UsedAttackers can improve odds by:
Applying a spellchecker to guess unknown words
Narrowing search to email addresses in contact list if the email application is running
Data can be protected by:Using a rubber case to absorb motionsSwitching to swiping-based keyboards
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Is this a Threat?Attacks could be disguised as any app
available on the marketOnly sensor that requires permission is
locationAccelerometer and gyroscope largely
ignored due to gaming
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How to Differentiate Taps
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Recognizing TapsTapPrints has to be trained to recognize tapsDifferent methods:
k-Nearest NeighborMultinomial Logistic RegressionSupport Vector MachinesRandom ForestsBagged Decision Trees
Combine all methods at end to get best results
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Collecting DataUsed four methods:
Icon TapsSequential LettersPangramsRepeated Pangrams
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Icon TapsAverages:
iPhone- 78.7%Nexus- 67.1%
Random guess is only 5%
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RepetitionsStabilizes at 20
taps/icon70% accuracy
reached at 12 tapsAttackers could
disguise as a gameCould also pre-train
to recognize other users’ taps
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Letter TappingHarder than icon taps
because letters are smaller and have less separation
Average prediction is 65.11% after training using pangrams
Random guess is only 3.8%
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Letter ConfusionMostly limited to
surrounding lettersCould be used in a
dictionary search to guess words
Some letters better than others, e.g. E vs. W
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Example of Pangram
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Sequential Letters
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Letter RepetitionMore repetitions
required because of smaller areas
150 taps to reach 50%
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Sensor Efficacy
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Possible SolutionsPause sensors when typingAgreements with developers to hold them
accountableHave users grant permission to use sensorsRubber cases to absorb motionSwiping-based keyboards
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ConclusionAttackers can use software to track
user inputTapPrints is just an early
implementationIn future, software will be much
more powerful
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BibliographyMiluzzo, Emiliano, Alexander Varshavsky,
Suhrid Balakrishnan, and Romit Roy Choudhury. "Tapprints: Your Finger Taps Have Fingerprints." MobiSys '12 Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services. MobiSys 2012, United Kingdom, Low Wood Bay, Lake District. New York: ACM, 2012. 323-36. Print.