1. Mouse Movement Project Customer: Larry Immohr Professor: Dr. Charles Tappert Team: Shinese Noble Anil Ramapanicker Pranav Shah Adam Weiss

2. Agenda Brief description of project Project Requirements Meetings Design Decisions Components Testing Strategy Challenges Questions

3. Description of Project Creation of a pattern recognition system for Mouse Movement Biometrics Creation of a pattern recognition system for Mouse Movement Biometrics Identify computer users thru their individual mouse movements Identify computer users thru their individual mouse movements Provides a feasibility study on whether this is a relevant way to track computer users behavior and identify them. Provides a feasibility study on whether this is a relevant way to track computer users behavior and identify them.

4. Description of Project Enough individuality in a users mouse movements to identify them Enough individuality in a users mouse movements to identify them ArcArc SpeedSpeed Acceleration / DecelerationAcceleration / Deceleration ClickingClicking Collect Data  Identify Features  Classify User Collect Data  Identify Features  Classify User

5. Mouse Movement Biometric System- High-Level View Mouse Movement Biometric System Enrolment Data and User Mouse action data Data Storage csv files Feature Extraction and Profile creation Feature Vector creation Classifies the feature vector. Finds the nearest neighbors User Mode User Mouse action data Enrolment Mode Identification Result Success Statistics

6. Project Requirements Capture data of individual mouse user Mouse Movement Mouse Click Store data Perform calculations to quantify mouse movements Utilize data to identify user

7. Meetings Team met via phone conference every Monday Team met via phone conference every Monday Constant communication via e-mailConstant communication via e-mail Meeting with client via phone conference every Tuesday Meeting with client via phone conference every Tuesday Communication via e-mailCommunication via e-mail Sharing documentationSharing documentation

8. Design Decisions Modular Format Modular Format Runs in Background Runs in Background Can be layered with any application Can be layered with any application Utilizes an enrollment program to get “fingerprint” of user Utilizes an enrollment program to get “fingerprint” of user Focused on a limited number of features due to time and resource constraints Focused on a limited number of features due to time and resource constraints Additional requirements can be built in as project continues Additional requirements can be built in as project continues

9. Components 3 modules of the program: 3 modules of the program: Data CaptureData Capture Feature ExtractionFeature Extraction ClassificationClassification

10. Design Decisions – 3 Programs Data Capture Module Feature Extractor Module Results Success Statistics Data Files Classifier Module Feature Vector Files

11. Data Capture – Architectural View User Task Area Mouse Monitoring Module Data Collection Module Standalone Application Data Files Tic-Tac-Toe GameBlank screenButton training

12. Data Capture - Enrollment

13. Data Capture - Data Mouse User Action Event Time in Milliseconds X Coordinate Y Coordinate

14. Feature Extraction Reads the raw data file Parses data into mouse curves and mouse clicks Compute individual curve and click measurements Creates a mouse profile of user Creates mouse profile measurements

15. Feature Extraction View MouseProfile Is a vector of curves and clicks? MouseCurves This is a vector of curves. A profile contains many curves MouseClicks This is a vector of clicks. A profile contains many clicks MousePoints Each curve is a vector of mouse points. CurveMeasure Each curve has one Measure object MouseData Each point is represented with MouseData:  Action  Time  x  y Each measure object can have many measures in it.  Speed  Length of the curve  Time of the curve  Curvature MousePoints Each click is having two mouse points. ClickMeasure Each click has one Measure object MouseData Each point is represented with MouseData:  Action  Time  x  y Each measure object can have many measures in it.  duration ProfileMeasure measure of many curves and clicks

16. Feature Extraction - Data

17. Classification Takes the feature vectors as the input Takes the feature vectors as the input Normalizes the data Normalizes the data Uses K-Nearest Neighbor algorithm for a test case Uses K-Nearest Neighbor algorithm for a test case Does a leave one out method for cross validation between many cases Does a leave one out method for cross validation between many cases Prints out the matching cases Prints out the matching cases Analyze the cross validation results and prints out the success statistics Analyze the cross validation results and prints out the success statistics

18. Testing Strategy Multiple releases Multiple releases Testing amongst team for bugs Testing amongst team for bugs Delivered to client after team testing Delivered to client after team testing Repeated for each release Repeated for each release For program data, all members input 5 samples of data For program data, all members input 5 samples of data

19. Challenges Establishment of clear goals Establishment of clear goals Change in scope of project Change in scope of project New project; many unknowns New project; many unknowns How to utilize enrollment program How to utilize enrollment program

20. Questions?