1 / 31

Keystroke Biometric Recognition Studies on Long-Text Input Under Ideal and Application-Oriented Conditions

Keystroke Biometric Recognition Studies on Long-Text Input Under Ideal and Application-Oriented Conditions. Mary Villani, Charles Tappert, and Sung-Hyuk Cha. Objective. For long-text input of 600 keystrokes Determine the viability of the keystroke biometric – two independent variables

Antony
Download Presentation

Keystroke Biometric Recognition Studies on Long-Text Input Under Ideal and Application-Oriented Conditions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Keystroke Biometric Recognition Studies on Long-Text Input Under Ideal andApplication-Oriented Conditions Mary Villani, Charles Tappert, and Sung-Hyuk Cha

  2. Objective • For long-text input of 600 keystrokes • Determine the viability of the keystroke biometric – two independent variables • Different entry modes – copy and free text • Different keyboards – desktop and laptop

  3. Advantages of Keystroke Biometric • Keyboards commonly used • Not intrusive • Inexpensive • Can Frequently Re-authenticate the User

  4. Keystroke Biometric System Components • Data Capture Applet • Feature Extractor • Pattern Classifier

  5. Data Capture Applet

  6. Sample Raw Feature Data Sample Raw Feature Data File Hello World

  7. 239 Feature Measurements • 78 Key Press Duration Measures (39 means and 39 standard deviations) • 70 Key Transition Type 1 Measures (35 means and 35 standard deviations) • 70 Key Transition Type 2 Measures (35 means and 35 standard deviations) • 21 Other Measures (percentages and rates)

  8. Type 1 and 2 Transition Measures

  9. Key Press Duration Features and Fallback Hierarchy Hierarchy tree for the 39 duration features (each oval), each represented by a mean and a standard deviation.

  10. Key Transition Featuresand Fallback Hierarchy Hierarchy tree for the 35 transition features (each oval), each represented by a mean and a standard deviation for each of the type 1 and type 2 transitions.

  11. Fallback for Few Samples • Mean and Standard Deviation Computation when number of samples n(i) is less than kfallback-threshold • Similar to NLP “backoff” statistics for n-grams

  12. Two Preprocessing Steps • Outlier removal • Remove samples > 2σ from µ • Prevents feature skewing from pauses • Standardization • Scales to range 0-1 to give roughly equal weight to each measure

  13. Pattern Classifier • Nearest Neighbor Classifier using Euclidean Distance

  14. Experimental DesignSix Main Experiments per Six Arrows

  15. Experimental DesignKeyboards (independent variable 1) • Desktop Keyboards – mostly (~100%) Dell desktops in a classroom environment • Laptop Keyboards – about 90% Dell laptops, some IBM, HP, Apple (greater variety of laptop than desktop keyboards)

  16. Experimental DesignInput Modes (independent variable 2) • Copy Task Input – specified text of about 600 keystrokes + corrections • Free Text Input – creation of arbitrary emails (at least 600 keystrokes)

  17. Subject Participation

  18. Participation By ExperimentEach subject entered 5 texts in at least two quadrantsA total of 36 participated in all four quadrants Desktop Laptop 1 52 Subjects 4 Copy 3 5 40 Subjects 47 Subjects 93 Subjects Free Text 41 Subjects 6 2 40 Subjects

  19. Five Sub Experiments for Each of the Six Arrows d & e a. Training & testing on data in quadrant at first end of arrow (leave-one-out procedure) b. Training & testing on data in quadrant at second end of arrow (leave-one-out procedure) c. Combining data at each arrow end (leave-one-out procedure) d. Training on first end – testing on second e. Training on second end – testing on first b a c

  20. Results Experiment 136 subjects participated in all quadrants

  21. Results Experiment 236 subjects participated in all quadrants

  22. Results Experiment 336 subjects participated in all quadrants

  23. Results Experiment 436 subjects participated in all quadrants

  24. Results Experiment 536 subjects participated in all quadrants

  25. Results Experiment 636 subjects participated in all quadrants

  26. 36 Subject Summary

  27. All Subject SummarySupports 36 Subject Results

  28. Conclusions • Best accuracies for same keyboard and same input mode • Accuracy dropped significantly for different keyboards or for different input modes • Accuracy for different input modes better than accuracy for different keyboards • Accuracy for copy mode somewhat better than accuracy for free-text mode • Accuracy decreased as the number of subjects increased

  29. Long-Text Input Applications • Identify the author of inappropriate email and possibly even IM • Authenticate the student taking online exams

  30. Future Work • Try more sophisticated classifiers • Neural Networks • Support Vector Machines • Explore the data with data mining

  31. Questions? • Thank you

More Related