1. Dynamic Authentication of Typing Patterns
2nd Quarter
Luke Knepper

2. Agenda
Background
Final Process
Experimentation
Current Results
Goals

3. The Dilemma Passwords are outdated Advanced biometrics are expensive
Need an alternative

4. A Solution Authenticate people by how they type
Typing patterns differ by person
Studies show that people can be authenticated by their typing patterns
Cheap and flexible to implement

5. The Problem
Usually will measure the user's keystrokes when typing in username & passwords
Commercial packages available
However, uses static text (username & password) → easy to hack
Need an improvement

6. The Fix
Generate random text and record keystrokes while the user types it
Not a static text segment → Makes it considerably harder to hack
No research found on dynamic text segments

7. Background
Measures users' typing patterns, compares to a previous standard
Technique first used in WWII
Works with ~90% Accuracy
Usually implemented in a neural network structure

8. Background

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

10. 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: result either meets threshold (admitted) or does not meet (rejected)

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

12. 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

13. Experimentation Neural Network Optimization:
Will train a neural network for each data file collected
Sample data will be sent through the neural network
Success vs. Failure ratio will be measured and compared between different network types

14. 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

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

16. Current Results Data collection Flash applet
Shows user segment of dynamic text, asks them to type it in a box below
Records their keystroke times
Sends keystroke data to server to be stored in separate files

17. Current Results Keystroke data file format:
For each keystroke, records the following:
Key-# / up-or-down / time-in-millis
Example: “65 U 22424”
Flexible format allows for different characteristics to be measured (e.g. time between strokes or time of depression)

18. Current Results Working on an automated testing system
First will train neural networks of each type for every data file as noted before
Then will record the results of each neural network through automated tested
Finally will compute statistics for the accuracy of the different types

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

20. Fin
Questions and wrap-up