Keystroke Biometric System IT 691 Capstone Project Keystroke Biometric System Client: Dr. Mary Villani (SUNY Farmingdale) Instructor: Dr. Charles Tappert Team 4: Tarjani Buch Andreea Cotoranu Eric Jeskey Florin Tihon

Introduction Keystroke biometric systems measure typing characteristics believed to be unique to an individual and difficult to duplicate; A keystroke biometric identification system was developed in the Seidenberg School in 2004 and has since gone through four project iterations with different graduate student teams; The system identifies subjects based on long-text (about 650 keystrokes) samples; Subjects using the same keyboard type (desktop or laptop) and entry mode (copy task or free text input) were identified with degrees of accuracy ranging from 98% to 100%;

Project Requirements Enhance the feature extractor component of the system to output feature data in a standard format; Implement new data collection schedule and collect new data samples to support a longitudinal study on identification experiments; Rerun some of the previous experiments with new data samples; Deliver feature data to back-end teams for additional identification and authentication experiments; Update project web site

System Specifications Pace University’s Utopia Server FTP client Java IDE (Borland’s JBuilder recommended) Java JDK (latest version)

System Components The keystroke biometric system consists of three main components: Java applet for collection of raw data Feature extractor Pattern classifier

System Components (Contd.) Java Applet for collection of raw data Subjects need to register in order to participate in the data collection process; There are four data entry tasks: Copy task on a Desktop Copy task on a Laptop Free text entry on a Desktop Free text entry on a Laptop

System Components (Contd.) Java Applet

System Components (Contd.) Feature Extractor Explanation to this interface can be included in one more slide under this heading.

System Components (Contd.) Pattern Classifier Again, explanation to this interface can be included in one more slide under this heading

New Feature Data Output

New Data Collection Schedule Entry Task Time T0 T1 T2 Copy-Laptop 5 samples Copy-Desktop Free-Text Laptop Free-Text Desktop Total # of Samples 20

Summary of Experimental Design Desktop Laptop Copy Task Free Text 1 4 3 6 5 2

Previous Identification Experiments Train Test Accuracy 1. Copy Task (36 subjects) a Desktop 99.4% b Laptop 100.0% c Combined 99.5% d 60.8% e 60.6% 2. Free Text 98.3% 98.1% 59.0% 61.0% 3. Desktop Copy Free Text 99.2% 89.3% 91.7% 4. Laptop 98.9% 86.2% 91.0% 5. Different Mode/Keyboard Lap Free Desk Copy 98.6% 51.6% 58.0% 6. Different Keyboard/Mode Desk Free Lap Copy 50.3% 52.1% These experiments tested optimal conditions (sub-experiments a and b), combined conditions (sub-experiment c) and less optimal conditions (sub-experiments d and e). All the experiments used the linguistic fallback model. Experiments for optimal and combined conditions used the “leave-one-out” classification method. These experiments revealed a very high level of accuracy, ranging between 98.3% and 100%. Experiments for the less optimal conditions used the “train on one and test on the other” classification method. These experiments revealed a level of accuracy ranging between 50.3% and 91.7%

New Identification Experiments Train/Test Accuracy T0 - T0 T0 - T1 T0 - T2 1. Copy Task (4 subjects) d Desktop/Laptop 100% 85% e Laptop/Desktop 95% 2. Free Text 3. Desktop Copy/Free Text Free Text/Copy 4. Laptop (4subjects) 90% 5. Different Mode/ Keyboard Desk Copy/ Lap Free 75% Lap Free/ Desk Copy 80% 6. Different Keyboard/ Mode Lap Copy/ Desk Free Desk Free/ Lap Copy Average 96% 98% Results support our hypothesis that high degrees of accuracy can be maintained over time

New Identification Experiments (Contd.) Train Test Task Accuracy Keystroke Biometric System Data Mining (Weka) T0 T1 Copy Desk 100% 95% Free Desk Copy Lap Free Lap 85% T2 90% 80%

Summary New experiments support previously documented accuracy findings; New experiments show that a high level of accuracy can be maintained over time; All experimental results are promising in that the system has the capability of solving identification problems and the potential for solving authentication problems; This can be skipped if we outline everything briefly in Deliverables/Accomplishments slide.

Future Recommendations Running experiments with a larger data pool collected under the discussed conditions should provide stronger evidence relative to the success of the keystroke biometric system for identifying and eventually for authenticating subjects. It would also provide more insight into how accuracy evolves from one data collection session to another over time. Although results of current studies support previous experimental results, it is recommended that more raw data be collected following the previously discussed data collection schedule involving two week intervals between data captures;

Communication A single face-to-face meeting with Dr. Mary Villani to get an understanding of the previous work and explore the potential for future developments Bi-weekly group meetings on IM Email communication with the stakeholders on a need basis Team Roles: Tarjani Buch – Data Collection Coordinator Andreea Cotoranu – Team Coordinator - Liaison Eric Jeskey – Architect / Designer Florin Tihon – Quality Officer / Tester

Deliverables/Accomplishments Technical Paper User Manual Enhanced Feature Extractor program to output feature vector in a standard format including normalization of feature values into the range 0-1 Raw data collection with team members as test subjects at two-week intervals Interval 1: T0 - November 3rd 2007 Interval 2: T1 - November 17th 2007 Interval 3: T2 - December 3rd 2007

Keystroke Biometric System Thank You http://utopia.csis.pace.edu/cs691/2007-2008/team4/