Louisiana Tech University

Louisiana Tech Research Corporation

The Penn State Foundation

Shrijit Sudhakar Joshi

Vir V Phoha

Asok Ray

Sampath Kumar Vuyyuru

Shashi Phoha

A method of identifying a user as an authorized user from free test text typed by that user into an input device. From the received test text, features associated with the typed text are extracted, such as timing data associated with alphanumeric letter pairs. These extracted features are compared to previously stored series of authorized user profiles, where the authorized user profiles were generated from a trial typing sample of alphanumeric data from each associated authorized user. The comparison identifies one of the authorized users with the user, and a score is derived to measure the strength of the comparison. If the score exceeds a threshold level, the user is identified as that authorized user.

Method and system of identifying users based upon free text keystroke patterns

Hidden Markov Models ('HMMs') are used to analyze keystroke dynamics measurements collected as a user types a predetermined string on a keyboard. A user enrolls by typing the predetermined string several times; the enrollment samples are used to train a HMM to identify the user. A candidate who claims to be the user provides a typing sample, and the HMM produces a probability to estimate the likelihood that the candidate is the user he claims to be. A computationally-efficient method for preparing HMMs to analyze certain types of processes is also described.

Hidden markov model ('HMM')-based user authentication using keystroke dynamics

Growing community of inventors

Ruston, LA, United States of America

Shrijit Sudhakar Joshi