1. Dynamic Authentication of Typing Patterns
Luke Knepper

2. Introduction Passwords are outdated Many biometrics are expensive
Typing patterns differ by person
People can be authenticated by their typing patterns
Cheap and flexible to implement

3. Agenda
Background
Final Process
Experimentation
Current Results
Goals

4. Background
Measures users' typing patterns, compares to a previous standard
Technique first used in WWII
Commercial packages available
Experiments have been done on static passwords → easy to hack
No research yet on dynamic text blocks

5. Process (front-end)
On account set-up, user will type large amounts of dynamic text
On subsequent log-ins, user will type small amount of dynamic text
User may still need to use username, password, etc.

6. Process (back-end)
Set-up data will be used to breed (i.e. train) a neural network
The optimal weight vector can be generated efficiently via back-propagation, genetic algorithms, parallel processing
Log-in data will be fed through neural network: either meets threshold (admitted) or does not meet (rejected)

7. Experimentation Requirements:
Must develop optimal neural network and threshold value for back-end
Must develop optimal GUI / Corpus size
Must determine accuracy

8. Experimentation Neural Network Optimization:
Develop online data collection applet
Collect massive amounts of data
Use data to train multiple neural network types
Test different network types to determine optimal network and threshold

9. Experimentation GUI / Corpus Optimization: Collect ~20 test subjects
Have them set up dummy accounts
Subjects attempt to log into their accounts and accounts of others
Repeat for different GUI layouts, corpus sizes

10. Experimentation Accuracy Testing:
Collect large number of test subjects
Subjects set up dummy accounts
Subjects attempt to log into their accounts and accounts of others on subsequent sittings (spaced out by 1 week and 1 month)
Measure final accuracy

11. Current Results Proof-of-concept program
Determines the mystery typer between two known users
Uses simple single-layer neural network
Correct 18 / 20 = 90%

12. Goals Final program will be: Easily implementable Difficult to crack
Accurate above 90%
Will be combined with password security to make inexpensive and secure system

13. Fin
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