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This work integrates deep learning and symbolic programming paradigms into a unified method for deploying applications to a neuromorphic system. The approach removes the need for coordination among disjoint co-processors by embedding both types entirely on a neuromorphic processor. This integration provides a flexible approach for using each technique where it performs best. A single neuromorphic...
Keystroke dynamics authentication is not as widely used compared to other biometric systems. In recent years, keystroke dynamic authentication systems have gained interest because of low cost and integration with existing security systems. Many different methods have been proposed for data collection, feature representation, classification, and performance evaluation. The work presents a detailed...
This study focuses on the development and evaluation of a new classification algorithm that halves the previously reported best error rate. Using keystroke data from 119 users, closed system performance was obtained as a function of the number of keystrokes per sample. The applications of interest are authenticating online student test takers and computer users in security sensitive environments....
The keystroke biometric classification system described in this study was evaluated on two types of short input - passwords and numeric keypad input. On the password input, the system outperforms 14 other systems evaluated in a previous study using the same raw input data. The three top performing systems in that study had equal error rates between 9.6% and 10.2%. With the classification system developed...
Data windows of keyboard input are analyzed to continually authenticate computer users and verify that they are the authorized ones. Because the focus is on fast intruder detection, the authentication process operates on short bursts of roughly a minute of keystroke input, while the training process can be extensive and use hours of input. The biometric system consists of components for data capture,...
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