Keystroke Biometric & Stylometry Systems

T EAMS T HE 2 & 4 M ICHAEL L. G ARGANO R ESEARCH D AY 9 TH A NNUAL 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

A

GENDA

 Team and Project Leader Introductions  KBS & Stylometry Projects Overview  Project Specifications & Deliverables  System Components & Enhancements  Results & Conclusions  Future Work

P

ROJECT

S

TAKEHOLDERS

Keystroke Biometric

Team Members



Vinnie Monaco

  Tyrone Allman Mino Lamrabat  Mandar Manohar

Stylometry

Team Members



Edyta Zych

  Omar Canales Vinnie Monaco  Thomas Murphy

Customers / SMEs

 Dr. Tappert  John Stewart  Robert Zack

Customers / SMEs

 Dr. Tappert  John Stewart

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

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

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

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

KBS Stylometry

P

ROJECT

D

ELIVERABLES Website Input System Feature Extractor Classifier Systems User Manuals & Documentation Input System Feature Extractor Presentation Technical Papers

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

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

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

B

ENCHMARK

R

ESULTS

: 18

SUBJECTS

, 180

SAMPLES Before After

N

ORMALIZATION

R

ESULTS ON

B

ENCHMARK

D

ATA Good Bad Still OK

A

NALYSIS

/ R

ESULTS

   40 students, 10 samples each from 1 test Weak training Keystroke and Stylometry biometrics

A

NALYSIS

/ R

ESULTS

   38 students, 20 samples from 2 tests Strong training Stylometry biometrics

FRR (%)

20

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

20 0 FRR (%) 100 0 FRR (%) 100

K

EYSTROKE VS

. S

TYLOMETRY

  

ROC C

URVE

38 students, 10 samples from 2 tests Weak training No equal error rate for stylometry

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

40 60 0 FRR (%) 100 0 FRR (%) 100

100

S

 

TYLOMETRY

C

OMBINED

D

ATA

24 STUDENTS , 10 W EAK SAMPLES T RAINING C OMBINED Authenticating students ~32% equal error rate   Authenticating test ~35% equal error rate

100 0 FRR (%) 100 0 FRR (%) 100

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

Q

UESTIONS

THANK YOU!

T

EAMS

K

EYSTROKE

& S

2 & 4

B

IOMETRIC TYLOMETRY

S

YSTEMS

Tyrone Mino

Allman,

Omar

Canales

Lamrabat,

Mandar

Manohar

Vinnie

Monaco,

Thomas

Murphy

John

Stewart,

Dr. Charles

Tappert

Robert

Zack,

Edyta

Zych