1. T EAMS 2 & 4 R ESEARCH D AY P RESENTATION P RESENTERS T EAMS 2 & 4 T HE M ICHAEL L. G ARGANO 9 TH A NNUAL R ESEARCH D AY P RESENTATION P RESENTERS E DYTA Z YCH & V INNIE M ONACO May 6, 2011 Seidenberg School of Computer Science and Information Systems Pace University, Graduate Center White Plains, New York Keystroke Biometric & Stylometry Systems

2. A GENDA Team and Project Leader Introductions KBS & Stylometry Projects Overview Project Specifications & Deliverables System Components & Enhancements Results & Conclusions Future Work

3. P ROJECT S TAKEHOLDERS Team Members Vinnie Monaco Tyrone Allman Mino Lamrabat Mandar Manohar Customers / SMEs Dr. Tappert John Stewart Robert Zack Team Members Edyta Zych Omar Canales Vinnie Monaco Thomas Murphy Customers / SMEs Dr. Tappert John Stewart Keystroke BiometricStylometry

4. T WO P ROJECTS A CT A S O NE, T WO T EAM L EADS Person Manager Facilitate Weekly Meeting Schedule Task Assignments Driving Everyday Activities Tech Training & Documentation Technical Manager Subject Matter Expert (SME) Technical Scope Design & Implementation of all System Enhancements Programming Tasks

5. O VERVIEW : K EYSTROKE B IOMETRIC S YSTEM Pace University has conducted over 8 years of research on Keystroke Biometrics The Keystroke Biometric System (KBS) can be used for both identifying and authenticating users from their typing rhythms Keystroke dynamics are the patterns of rhythm and timing created when a person types, including: Overall speed Variations of speed moving between specific keys Common errors The length of time that keys are depressed (duration) This semester’s work focuses solely on the KBS as it relaters to an online test taking environment

6. O VERVIEW : S TYLOMETRY Stylometry is the study of the unique linguistic styles and writing behaviors of individuals in order to determine authorship Stylometry uses statistical pattern recognition, and artificial intelligence techniques Stylometry features typically used to analyze text include word frequencies and identifying patterns in common parts of speech This semester’s work focuses on text input being used in conjunction with the keystroke analysis to improve authentication results including Determining authorship in documents (Beneficial academically to assist with on-line test taking) Protecting against plagiarism through a third party

7. P ROJECT S PECIFICATIONS Work closely with our project customer to define the most appropriate Keystroke & Stylometry Features and add additional features to assist in validating/authenticating the identity of students taking an online exam Extract the selected Feature Set for Keystroke Biometric and Stylometry Analysis and run experiments to measure program performance utilizing the enhanced systems: Input System, Feature Extractor and Classifier Run experiments and tests on the data collected to support the identification of subject and online test-taker authorship

8. P ROJECT D ELIVERABLES Systems User Manuals & Documentation Website Presentation Technical Papers Input System Feature Extractor Input System Feature Extractor Classifier KBS Stylometry

9. O VERVIEW OF S YSTEM C OMPONENTS Input System Captures keystroke and stylometry data in an online test format Feature Extractor Measures raw data to obtain a feature vector for each sample Classifier Uses feature vectors to test authentication

10. I NPUT S YSTEM E NHANCEMENTS Upgraded from a Java Applet to a standalone java program. Implemented a user management system to simulate an online test taking environment Change to test taking format, instead of free text or copying tasks Moved to a more general XML data format, to handle both keystroke and stylometry data More restrictions in place on how users interact with the system Disable cut/copy/paste ability Users must complete the test in full Capture and log keystrokes from every successful login attempt

11. F EATURE E XTRACTION E NHANCEMENTS Feature extraction implemented in the functional language Clojure Easy integration with Java front end Better data handling, filtering, and mapping capabilities New Normalization method tested Old formula New formula Improved outlier removal Integrated stylometry and keystroke features

12. B ENCHMARK R ESULTS : 18 SUBJECTS, 180 SAMPLES Before After

13. N ORMALIZATION R ESULTS ON B ENCHMARK D ATA BadGoodStill OK

14. A NALYSIS / R ESULTS 40 students, 10 samples each from 1 test Weak training Keystroke and Stylometry biometrics

15. A NALYSIS / R ESULTS 38 students, 20 samples from 2 tests Strong training Stylometry biometrics FRR (%) FAR (%)

16. K EYSTROKE C OMBINED D ATA 38 students, 20 samples each from 2 tests Weak training ~11% equal error rate 38 students, 20 samples each from 2 tests 2 samples combined yielding 10 samples each Weak training ~5% equal error rate FRR (%) FAR (%) FRR (%) FAR (%) 0 100 20 0 100 20

17. K EYSTROKE VS. S TYLOMETRY ROC C URVE 38 students, 10 samples from 2 tests Weak training No equal error rate for stylometry

18. S TYLOMETRY C OMBINED D ATA 40 students, 10 samples each from 1 test No equal error rate 30 students, 30 samples each from 3 tests 6 samples combined yielding 5 samples each ~30% equal error rate FRR (%) FAR (%) 0 100 60 FRR (%) FAR (%) 0 100 40

19. 24 STUDENTS, 10 SAMPLES C OMBINED W EAK T RAINING S TYLOMETRY C OMBINED D ATA Authenticating students ~32% equal error rate Authenticating test ~35% equal error rate FRR (%) FAR (%) 0 100 FRR (%) FAR (%) 0 100

20. F UTURE W ORK Keystroke and Stylometry Biometrics Improve stylometry authentication results by identifying important features Combined more samples to obtain stylometry features on longer text input Determine if samples may be authenticated to a test, as opposed to the individual Data Collection Modify the input system to eliminate some problems with giving an online test Authenticate with first/last name only Ability to traverse the questions in the test Integrate keystroke authentication with users logging into the system

21. Q UESTIONS

22. K EYSTROKE B IOMETRIC & S TYLOMETRY S YSTEMS T EAMS 2 & 4 K EYSTROKE B IOMETRIC & S TYLOMETRY S YSTEMS THANK YOU! Tyrone Allman, Omar Canales Mino Lamrabat, Mandar Manohar Vinnie Monaco, Thomas Murphy John Stewart, Dr. Charles Tappert Robert Zack, Edyta Zych