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Keystroke Dynamics Jacob Wise and Chong Gu

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Introduction ● People have “unique” typing patterns – “Unique” in the same way that fingerprints aren't proven unique ● Typing patterns could be used for authentication – Stronger than password – Harder to copy – Can use challenge-response ● Inexpensive

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Previous Work ● Neural Networks – Less mainstream approach – Papers co-authored by M.S. Obaidat ● “Traditional” Approach – Reference Signatures computed by calculating the Mean and Standard Deviations – Measures “distance” between Reference Signature and Test Signature – Use digraph/trigraph – Rick Joyce & Gopal Gupta (1990); F. Monrose & a. Rubin (1997); F. Bergadano, D. Bunetti, and C. Picardi (2002)

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First problem - Collecting Data ● Built-in.NET DateTime class – Precise only to about 10 milliseconds ● Methods from kernel32.dll – About 15 significant digits (don't know for sure)

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First Prototype ● Timing Data for all fields – User Name – Password – Full Name ● Mistakes not allowed ● Signature object is serialized and saved to a file

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The World of Neural Networks ● User Name / Password / Full Name unsuitable – Can't train a neural network on only positive examples – Would need to collect break-in attempts by other users ● Hence the “Counterexample” option in the first prototype ● Everyone-Types-The-Same-Thing works better – Hence the passage collection form...

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The Passage Collection Form

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Passage Analysis Form ● Tool to help analyze collected keystroke data – Data is in.psig (PassageSignature) and.signature (Signature) files ● We hope this tool will be used and extended in future work on this project ● Tabs for BPN (Back-Propagation Network), more traditional analyses, and others that are yet to come

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Passage Analysis Form

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[neural networks] ● Explain BPN basics ● This started as just a first step ● Ended up taking the whole time to tune

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“Traditional” Approach ● Reference Signature – Computed by calculating the mean and standard deviation of samples each user has provided – Based on Press Time or Flight Time – Samples that are too far off (greater than a certain threshold above the mean) are discarded. The Means are recalculated. ● This value needs to be tuned ● 3 std results in 0.85% of samples being discarded ● 2 std results in 5% of samples being discarded

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“Traditional” Approach - Reference Signatures based on Flight Time

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“Traditional” Approach - Reference Signatures based on Press Time

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“Traditional” Approach - Reference Signatures We have noticed that there is a bigger variance between users if we base our Reference Signatures on Flight Times.

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“Traditional” approach - the Verifier ● Two approaches have been considered, but neither is up and running – Comparing individual Press/flight time of test signature with the Mean Reference Signature. A press/flight time is considered to be valid if it is within x profile standard deviations of the mean reference digraph. (where x needs to be tuned) – Comparing the magnitude of difference between the mean reference signature (M) and the test signature (T). A certain threshold for an acceptable size of the magnitude is required. A user with a bigger variability of his/her signatures, a bigger threshold value should be used. ● This approach has had some good results ● Again, the threshold value needs to be tuned.

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Conclusion ● We have... – Done lots of work but just barely scratched the surface – Focused getting some usable analysis tools up and running – Implemented fairly standard algorithms according to previous research ● There is a lot of work to be done!

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Epilogue ● Papers that excite us and into which we didn't have time to seriously delve: – “User Authentication through Keystroke Dynamics” Bergadano, Gunetti, Picardi (2002) – “Password hardening based on keystroke dynamics” Monrose, Reiter, Wetzel (2001) ● Not just authentication

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