1. 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

2. 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%;

3. 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

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

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

6. 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

7. System Components (Contd.)
Java Applet

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

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

10. New Feature Data Output

11. 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

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

13. 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%

14. 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

15. 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%

16. 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.

17. 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;

18. 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
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

19. 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

20. Keystroke Biometric System Thank You