TEAMS 2 & 4THE MICHAEL L. GARGANO 9TH ANNUALRESEARCH DAY PRESENTATION
PRESENTERSEDYTA ZYCH & VINNIE MONACO
May 6, 2011Seidenberg School of Computer Science and Information SystemsPace University, Graduate CenterWhite Plains, New York
Keystroke Biometric& Stylometry Systems
AGENDA Team and Project Leader Introductions KBS & Stylometry Projects Overview Project Specifications & Deliverables System Components & Enhancements Results & Conclusions Future Work
PROJECT STAKEHOLDERS
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 Biometric Stylometry
TWO PROJECTS ACT AS ONE, TWO TEAM LEADS
Person ManagerFacilitate Weekly Meeting ScheduleTask AssignmentsDriving Everyday ActivitiesTech Training & Documentation
Technical ManagerSubject Matter Expert (SME)Technical ScopeDesign & Implementation of all System
EnhancementsProgramming Tasks
OVERVIEW: KEYSTROKE BIOMETRIC SYSTEM 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
OVERVIEW: STYLOMETRY 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
PROJECT SPECIFICATIONS 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
PROJECT DELIVERABLES
Systems
User Manuals & Documentation
Website
Presentation
Technical Papers
Input SystemFeature Extractor
Input SystemFeature ExtractorClassifier
KBSStylometry
OVERVIEW OF SYSTEM COMPONENTS 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
INPUT SYSTEM ENHANCEMENTS 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
FEATURE EXTRACTION ENHANCEMENTS 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
BENCHMARK RESULTS: 18 SUBJECTS, 180 SAMPLESBefore
After
NORMALIZATION RESULTS ON BENCHMARK DATA
BadGood Still OK
ANALYSIS / RESULTS 40 students, 10 samples each from 1 test Weak training Keystroke and Stylometry biometrics
ANALYSIS / RESULTS 38 students, 20
samples from 2 tests Strong training Stylometry
biometrics
FRR (%)
FAR
(%)
KEYSTROKE COMBINED DATA 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
KEYSTROKE VS. STYLOMETRY ROC CURVE 38 students, 10 samples from 2 tests Weak training No equal error rate for stylometry
STYLOMETRY COMBINED DATA 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
(%)
0100
40
24 STUDENTS, 10 SAMPLES COMBINEDWEAK TRAINING
STYLOMETRY COMBINED DATA
Authenticating students ~32% equal error rate
Authenticating test ~35% equal error rate
FRR (%)
FAR
(%)
0100
100
FRR (%)
FAR
(%)
0100
100
FUTURE WORKKeystroke 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
QUESTIONS
TEAMS 2 & 4KEYSTROKE BIOMETRIC
& STYLOMETRY SYSTEMS
THANK YOU!
Tyrone Allman, Omar CanalesMino Lamrabat, Mandar ManoharVinnie Monaco, Thomas Murphy
John Stewart, Dr. Charles TappertRobert Zack, Edyta Zych