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Dynamic Authentication of Typing Patterns

This paper introduces a dynamic authentication system using typing patterns, which are unique to individuals. The process involves collecting user's typing patterns during account set-up and using a neural network to authenticate their subsequent log-ins. The system aims to be easily implementable, secure, and accurate above 90%. Commercial packages and experiments on static passwords have proven to be easily hacked, making this dynamic system an effective alternative. The goal is to combine it with password security to create an inexpensive and secure solution.

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Dynamic Authentication of Typing Patterns

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  1. Luke Knepper Dynamic Authentication of Typing Patterns

  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 Introduction • Passwords are outdated • Many biometrics are expensive • Typing patterns differ by person • People can be authenticated by their typing patterns • Cheap and flexible to implement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 • Who wants a cookie?

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