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Mouse Movement Biometric System
Proceedings of Student-Faculty Research Day, CSIS, Pace University, May 3rd, 2013
Pedro Xavier de Oliveira, Venugopala Channarayappa, Eamonn O’Donnel, Bappaditya
Sinha, Aswinkumar Vadakkencherry, Tushar Londhe, Umesh Gatkal, Ned Bakelman,
Vinnie Monaco, and Charles Tappert
Seidenberg School of CSIS, Pace University, White Plains NY, 10606, USA
Introduction
•User authentication
 Password
 Token
 Fingerprint
•Behavioral Biometric systems
 Provide data specific to every user’s characteristics
•The mouse biometric system
 Continuous dynamic authentication
 No additional hardware requirement
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Related work
• Different approaches available to collect data
 Using pre-defined grid systems
 User specific gaming scenarios
 Capture data using specific applications
• No detailed work around capturing natural mouse
movements.
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Current Research Approach
• Mouse Movement Biometric System
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Generic Mouse Movement Data Capture.
User accessing a computer for a specific time duration.
Any natural mouse movement event captured.
Unpredictable behavior but completely user behavioral.
Feature extractor to capture / convert all mouse events.
Stronger continuous user authentication not restricted to login time.
Features Measurements
• Researched and identified mouse movement categories and features
• Mouse Movement Categories
• Mouse Trajectory
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System wake up
Move and click
Highlight
Drag and drop
Mouse Click
Mouse wheel spin or scroll
Applications accessed
Mouse activity time
Trajectories features
• Number of trajectory points
• Time of the trajectory
• Point-to-point distance in the trajectory
• Length of the trajectory
• Point-to-point velocity in the trajectory
• Point-to-point acceleration in the trajectory
• Point-to-point direction angle change
• Number of inflection points in the trajectory
• Curviness of the trajectory
mean (average), median, minimum, maximum, standard deviation
45 features for each action type, totaling 135 features
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Trajectories features..(contd)
• Trajectory ratio features
number of wake - up actions
total number of trajectory actions
number of move - and - click actions
total number of trajectory actions
number of highlight and drag - and - drop actions
total number of trajectory actions
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Mouse Click features
• Type of mouse click
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Left click
Right click
Double click
Highlight
Drag-and-drop
• Ratio of clicks (4 features)
• Average of clicks (5 features)
Mean, Median, Minimum, Maximum dwell from left/right clicks (12
features)
Mean, Median, Minimum, Maximum transaction time of all double
click/drag-and-drop (8 features)
Total of 29 mouse click features
Mouse Wheel Spin features
• Mouse wheel event
 Scroll up
 Scroll Down
• Ratio of scroll up/down to total of mouse wheel events
• Ratio of time spent in wheel event
Mean, Median, Minimum, Maximum duration of a mouse wheel
event (12 features)
Mean, Median, Minimum, Maximum distance for a mouse wheel
event (12 features)
Mean, Median, Minimum, Maximum speed of a mouse wheel event
(12 features)
Total of 39 mouse wheel features
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Applications features
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The number of applications accessed
The name of the most used (in time spent) application
The name of the second most used application
The name of the third most used application
Mouse Activity Time
• The Fraction of session time involving mouse activity
• Fraction of mouse activity time used for wheel spin event
• Fraction of scroll activity time used during mouse selection (shift key
+ mouse move event)
• Fraction of mouse activity time used for mouse move events
• Fraction of activity time used in scroll up event
• Fraction of activity time used in scroll down event
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Features List Summary
• Total of 217 features
 138 trajectory
 29 mouse click
 40 mouse wheel spin
 4 application accessed
 6 mouse activity time
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Mouse biometric system
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Feature extraction system
• Use data collected by the Online Biologger
(http://vmonaco.com/biometrics/logger)
• Convert CSV to generic XML mouse data
• Feature extractor
 Categorize / extract based on mouse events.
 Generate a CSV file with data generated
 Option of parsing multiple user session input XMLs.
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Authentication Classification
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Transform a multi-class problem to a two-class problem
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Effective in large open biometric systems like mouse movement.
Authentication comparison of various user samples versus the
trained previous samples
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Calculate / Derive the differences of vectors
Authentication result based on pre-calculated False Accept Rate (FAR)
and False Reject Rate (FRR)
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You are authenticated
You are not authenticated
Ideally no two user sample can have a 100% match!
System Test
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Continuous authentication based on detection of user change over
time.
Different tasks needs to analyzed / extracted to test the generic user
behavioral mouse biometric system.
Three different scenario sets are identified
 Edit / Modify tasks
 Browser tasks
 Gaming scenarios
Edit / Modify Tasks
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Edit or Modify tasks include writing new document (typing),
modify or rewrite content.
 Light copy editing
 Minor-Minor editing
 Moderate copy editing
 Heavy copy editing
 Major changes and document rewrite
Browser Tasks
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Generic scenario exercised by every computer user.
Heavy mouse movement / usage.
User differentiated by the way of browsing (internet content)
behavior.
The behaviors include mouse actions mouse clicks, mouse
pause (reading content), scroll, right click etc.
User Behavior can be tracked for various actions – open/close
browser, open/close regular websites, search etc.
Gaming Scenarios
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Strong contender for all aged user data capture.
User differentiated by the mouse activities performed
during the game play.
The behaviors include mouse actions - mouse clicks, mouse
pause (reading rules), scroll, right click etc.
Gaming scenarios focus on the performance of the system
in authenticating the game player when all participants play
the same game.
Conclusion
• Primary focus of this research was to identify various mouse
biometric features
• 217 feature sets identified!
• Developed a java feature extractor system.
• Capture / extract generic mouse movements
• Future work
• Future groups can further investigate / improve the quality of
the software by using more classification results with larger
amount of data.
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Thank You!
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