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TapPrints : Your Finger Taps Have Fingerprints PowerPoint Presentation, PPT - DocSlides

Presented by: Tom Staley. About. Paper by. Emiliano. . Miluzzo. Alexander . Varshavsky. Suhrid. . Balakrishnan. Romit. Roy . Choudhury. Originally presented at MobiSys2012, June 27, 2012. Introduction. ID: 737580

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Slide1

TapPrints: Your Finger Taps Have Fingerprints

Presented by: Tom Staley

Slide2

About

Paper by

Emiliano

Miluzzo

Alexander

Varshavsky

Suhrid

Balakrishnan

Romit

Roy

Choudhury

Originally presented at MobiSys2012, June 27, 2012

Slide3

Introduction

Determining location of screen taps using accelerometer and gyroscopes

Could lead to attackers using this info to track inputs

“

TapPrints

- a framework for inferring location of taps on mobile devices”

Slide4

Current State of Sensors

Mobile sensors becoming more powerful

Many 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

Slide5

Using Gyroscopes

Slide6

TapPrints

Implemented on Google Nexus S, Apple

iPhone

4, Samsung Galaxy Tab 10.1

Over 40,000 taps collected from 10 users over 4 weeks

80-90% accuracy, enough to guess a password

Slide7

How Data Could be Used

Attackers 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 motions

Switching to swiping-based keyboards

Slide8

Is this a Threat?

Attacks could be disguised as any app available on the market

Only sensor that requires permission is location

Accelerometer and gyroscope largely ignored due to gaming

Slide9

How to Differentiate Taps

Slide10

Recognizing Taps

TapPrints

has to be trained to recognize taps

Different methods:

k-Nearest Neighbor

Multinomial Logistic Regression

Support Vector Machines

Random Forests

Bagged Decision Trees

Combine all methods at end to get best results

Slide11

Collecting Data

Used four methods:

Icon Taps

Sequential Letters

Pangrams

Repeated Pangrams

Slide12

Icon Taps

Averages:

iPhone

- 78.7%

Nexus- 67.1%

Random guess is only 5%

Slide13

Repetitions

Stabilizes at 20 taps/icon

70% accuracy reached at 12 taps

Attackers could disguise as a game

Could also pre-train to recognize other users’ taps

Slide14

Letter Tapping

Harder 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%

Slide15

Letter Confusion

Mostly limited to surrounding letters

Could be used in a dictionary search to guess words

Some letters better than others, e.g. E vs. W

Slide16

Example of Pangram

Slide17

Sequential Letters

Slide18

Letter Repetition

More repetitions required because of smaller areas

150 taps to reach 50%

Slide19

Sensor Efficacy

Slide20

Possible Solutions

Pause sensors when typing

Agreements with developers to hold them accountable

Have users grant permission to use sensors

Rubber cases to absorb motion

Swiping-based keyboards

Slide21

Conclusion

Attackers can use software to track user input

TapPrints

is just an early implementation

In future, software will be much more powerful

Slide22

Bibliography

Miluzzo

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.

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