Fast Continuous User Authentication Using Distance Metric Fusion of Free-Text Keystroke Data

Blaine Ayotte, Jiaju Huang, Mahesh K. Banavar, Daqing Hou, Stephanie Schuckers; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Keystroke dynamics are a powerful behavioral biometric capable of determining user identity and for continuous authentication. It is an unobtrusive method that can complement an existing security system such as a password scheme and provides continuous user authentication. Existing methods record all keystrokes and use n-graphs that measure the timing between consecutive keystrokes to distinguish between users. Current state-of-the-art algorithms report EER's of 7.5% or higher with 1000 characters. With 1000 characters it takes a longer time to detect an imposter and significant damage could be done. In this paper, we investigate how quickly a user is authenticated or how many digraphs are required to accurately detect an imposter in an uncontrolled free-text environment. We present and evaluate the effectiveness of three distance metrics individually and fused with each other. We show that with just 100 digraphs, about the length of a single sentence, we achieve an EER of 35.3%. At 200 digraphs the EER drops to 15.3%. With more digraphs, the performance continues to steadily improve. With 1000 digraphs the EER drops to 3.6% which is an improvement over the state-of-the-art.

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[bibtex]
@InProceedings{Ayotte_2019_CVPR_Workshops,
author = {Ayotte, Blaine and Huang, Jiaju and Banavar, Mahesh K. and Hou, Daqing and Schuckers, Stephanie},
title = {Fast Continuous User Authentication Using Distance Metric Fusion of Free-Text Keystroke Data},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}