USER IDENTIFICATION THROUGH KEYSTROKE BIOMETRICS AT AN INTERNET SCALE
Veazey, Mark W.
Fulp, John D.
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Identification of users on the internet has broad-reaching implications in the computer science discipline regarding cyber security and privacy. Keystroke biometrics leverages the unique dynamics of how a user types to perform identification; however, current methods of authentication and identification using keystroke dynamics do not scale well beyond a few hundred users. This thesis investigates the feasibility of using conventional machine learning and deep learning techniques to identify users at an internet scale. By analyzing free-text keystroke information from a collection of over 100,000 users, several methods to perform user identification and profiling are identified, with a focus on determining how the size of the dataset affects identification accuracy. This thesis includes a novel method of representing keystroke data in a two-dimensional format suitable for a convolutional neural network, and it examines to what extent keystroke biometrics has implications for privacy on the internet.
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Richardson, Nathan J.; Burke, Lucas J. (Monterey, CA; Naval Postgraduate School, 2019-06);The power of anonymity on the internet is being leveraged by criminals, online trolls, and malicious hackers. Cyber criminals masquerade as legitimate users in order to steal valuable private data, commit fraud, and steal ...
Goodwin, Peter T. (Monterey, CA; Naval Postgraduate School, 2020-03);The study of keystroke dynamics began decades ago, but the field has become increasingly relevant due to current events that have increased public awareness of the erosion of internet privacy. Web servers of 2020 are highly ...
Fleming, Samuel B. (Monterey, California: Naval Postgraduate School, 2014-03);Keystroke analysis has been an accepted method for user identification and authentication since the early 1980s. Most of the research in this field of biometrics has focused on traditional computer keyboards, with very few ...