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Keystroke Biometric SystemClient: Dr. Mary VillaniInstructor: Dr. Charles Tappert
Team 4 Members: Michael Wuench ; Mingfei Bi ; Evelin Urbaez ; Shaji Mary Varghese ; Michael Tevnan
Contents
1. Introduction1. Introduction
2. New System2. New System
3. Experimental Results 3. Experimental Results
4. Conclusion4. Conclusion
5. Future Studies5. Future Studies
6. Demo6. Demo
Previous Work
Pace University has 5+ years in keystroke biometrics research.
Built a complex system of interworking JAVA and PHP programs to support academic research in biometrics.
System can successfully identify and authenticate individuals with a relatively high degree of accuracy especially during same time periods.
Introduction
Objectives
• Modifies the existing systems toward practical usage.
• Attempts to verify users taking an online test based on the characteristics of their typing.
• Analyze results on new input data.
• Present possible methods for determining instant authentication.
Current Study
Results
New System Overview
SubjectSubjectRegistrationRegistration
SubjectSubjectRegistrationRegistration
Classifier (BAS)Classifier (BAS)Classifier (BAS)Classifier (BAS)
Test Taker AppletTest Taker AppletSubjectSubject
DemographicDemographic
KeystrokeKeystrokeEntryEntry
KeystrokeKeystrokeEntryEntry
Raw FileRaw File
Actual Text Actual Text FileFile
Client Client Reviews TestReviews Test
Feature Extractor (BioFeature)Feature Extractor (BioFeature)Feature Extractor (BioFeature)Feature Extractor (BioFeature)
New System
New System consists of the following:
1. A PHP Website registers the user.
2. A modified Java applet captures 300 keystrokes and produces two files: a raw data file and a text file.
3. A Java program, BioFeature, extracts 239 feature measurements.
4. A Java program, Biometric Authentication System (BAS), performs authentication tests.
Test-Taker Authentication System
Feature Extraction – BioFeature Extracts 239 features from raw data collected
from applets Ex. Features file
Authentication Classifier Uses 2 features files One is trained-on and the other is tested-on. Returns False Acceptance Rate (FAR), False
Rejection Rate (FRR) & combined performance Ex. BAS results (html file)
Authentication Classifier
Authentication Transformation
feature space (left) feature distance space (right)
Experimental Design: Data Sets
1. Team member Data: 5 samples of free text using enrollment applet
(~650 keystrokes) 5 samples of free text data using test-taker
applet (~300 keystrokes)2. Outsider Sample:
5 samples of free text data using test-taker applet (~300 keystrokes)
3. Original 36 subjects from 2006 Study: 5 samples of laptop free text using
enrollment applet (~650 keystrokes)
Test | Train
5 | 5
5 | 5
6 | 5
5 | 6
FRR
0.0%(0/50)
6.0%(3/50)
0.0%(0/60)
12.0%(6/50)
FAR
4.8%(12/250)
1.2%(3/250)
11.2%(42/375)
0.4%(1/250)
Performance
96.0%(288/300)
98.0%(294/300)
90.3%(393/435)
97.7%(293/300)
1
2
3
4
Experimental Results
Biometric Authentication System (BAS) results using test and enrollment samples (5 per subject) collected in the fall of 2008
Study 1: Fall 2008 Team with Outsider
Performance is high (at least 95%) with same subject testing
No significant difference in results due to keystroke length (300 vs 650)
Immediate drop in performance when an subject that is not enrolled is used.
Increased number of subjects is recommended
Study 1 Conclusions
Study 2 (partial)
Test | Train FRR FAR Performance
10 | 5 0.0% (0/225) 27.7% (277/1000) 77.4% (948/1225)
10 | 10 8.4% (19/225) 8.5% (85/1000) 91.5% (1121/1225)
10 | 15 7.1% (16/225) 5.9% (59/1000) 93.9% (1150/1225)
10 | 20 25.3% (57/225) 1.8% (18/1000) 93.9% (1150/1225)
10 | 25 29.3% (66/225) 1.4% (14/1000) 93.5% (1145/1225)
10 | 30 44.0% (99/225) 0.9% (9/1000) 91.2% (1117/1225)
10 | 36 39.6% (89/225) 0.6% (6/1000) 92.2% (1130/1225)
Study 2: Original-36 Training-on Tests
Testing on combined fall 2008 enrollment and test-taker samples (10 per subject) and training on original-36 subject samples (5 per subject).
Study 2 Conclusions
Again, keystroke length has little effect on results.
When the number of subject is large (30+), it produces a very low FAR (should be as low as possible for maximum security).
Performance increases (above 90%), FAR decreases, and FRR increases as # of subject is 10 or more.
Study 3 (partial)
Test | Train FRR FAR Performance
5 | 10 10.0% (5/50) 11.2% (28/250) 89.0% (267/300)
10 | 10 3.0% (3/100) 23.2% (232/1000) 78.6% (865/1100)
15 | 10 2.7% (4/150) 21.6% (216/1000) 80.8% (930/1150)
20 | 10 5.0% (10/200) 57.5% (575/1000) 51.3% (615/1200)
25 | 10 2.0% (5/250) 68.1% (681/1000) 45.1% (564/1250)
30 | 10 1.3% (4/300) 72.0% (720/1000) 44.3% (576/1300)
36 | 10 0.3% (1/360) 78.5% (785/1000) 42.2% (574/1360)
Study 3: Original-36 Testing-on Tests
Testing on original 36 subject samples (5 per subject) and training on combined fall 2008 enrollment and test-taker samples (10 per subject).
Study 3 Conclusions
Yet again, keystroke length has little effect on results.
Overall poor performance indicates that system requires adequate training data.
FAR increases substantially, and FRR decreases as # of subject is 10 or more.
Study 2 and Study 3 hint that 30 or more subjects will yield a more reliable authentication.
Future Studies
Convert the current Java programs to web applications using J2EE or PHP.
Further testing should be done with at least 30 enrolled subjects.
Use the test taker applet as individual samples to test against a large enrollment database.
Continue to modify the Authentication Classifier (BAS) to implement the proposed k-nearest-neighbor procedure. (NEXT)
Simple k-nearest-neighbor procedure
W
4
8
7
19>=Accept
B
6
2
3
11
1
2
3
Totals
Proposed authentication using k-nearest-neighbor procedure.
Matching sets:
[W B B W B B W W B B]
[W W B W W W W W B W]
[W B W W B B W W W W]
K = 10 (the 10 nearest neighbors) W = within (accept) class B = between (reject)
Demo: Test-Taker Authentication System
How to access the system (Taking the Test ):
User must first enroll into the system.
Click on the Web Link (http://utopia.csis.pace.edu/cs691/2008-2009/team4/testtakersite).
The enter same name you enrolled with and take the test in the applet
Demo: Taking the Test
Presented with five questions and must provide five answers.
Answer should be more than 50 words, based on the assumption that words are approximately 6 keystrokes in length.
Once completed, click on “Submit”
If 50 words are not meet, user will be presented with an error.
Demo: New Online Test System
Logging In
New Online Test System
Test Applet
New Online Test System
Test Applet (continued)
New Online Test System
User reached at least 300 keystrokes
Demo: Test Applet
User did reach or surpass the 300 keystrokes
Q&A Thank You!Pace University
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