The Penn State Research Foundation

Louisiana Tech University

US Government as Represented by the Secretary of the Army

Asok Ray

Vir V Phoha

Soumalya Sarkar

Thyagaraju Damarla

Shrijit Sudhakar Joshi

Shashi Phoha

Sampath Kumar Vuyyuru

Sunil Babu

Shashi P Phoba

A system for detection of human or vehicle activity comprising at least one sensor adapted to generate a signal and at least one processor operating to denoise the signal; generate an autocorrelation of the signal; partition the signal into a predetermined number of overlapping segments to form a time series of data; generate symbols for the overlapping segments; compare the pattern of generated symbols with known predetermined patterns of symbols representing human or vehicular activity; determine whether a threshold probability is exceeded which attributes the data signal to human or vehicular activity; analyze the patterns presented in the data signal by transforming the patterns of symbols into states; determine the transitions between states; and classify the signal as to being attributable to human or vehicular activity based upon the transitions between states. A method of detection and classification of sensor data signals via detecting patterns using time series analysis.

Apparatus and method for activity detection and classification from sensor data

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

We develop a system consisting of a neural architecture resulting in classifying regions corresponding to users' keystroke patterns. We extend the adaptation properties to classification phase resulting in learning of changes over time. Classification results on login attempts of 43 users (216 valid, 657 impersonation samples) show considerable improvements over existing methods.

System and method for classifying regions of keystroke density with a neural network

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State College, PA, United States of America

Asok Ray