Keystroke dynamics study the way in which users input text via their keyboards, which is unique to each individual, and can form a component of a behavioral biometric system to improve existing account security. Keystroke dynamics systems on free-text data use n-graphs that measure the timing between consecutive keystrokes to distinguish between users. Many algorithms require 500, 1,000, or more keystrokes to achieve EERs of below 10%. In this paper, we propose an instance-based graph comparison algorithm to reduce the number of keystrokes required to authenticate users. Commonly used features such as monographs and digraphs are investigated. Feature importance is determined and used to construct a fused classifier. Detection error tradeoff (DET) curves are produced with different numbers of keystrokes. The fused classifier outperforms the state-of-the-art with EERs of 7.9%, 5.7%, 3.4%, and 2.7% for test samples of 50, 100, 200, and 500 keystrokes.
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A Dataset of Noisy Typing on QWERTY Keyboards
Text entry is a common and important part of many intelligent
user interfaces. However, inferring a user’s intended text from their
input can be challenging: motor actions can be imprecise, input
sensors can be noisy, and situations or disabilities can hamper a
user’s perception of interface feedback. Numerous prior studies
have explored input on touchscreen phones, smartwatches, in midair,
and on desktop keyboards. Based on these prior studies, we
are releasing a large and diverse data set of noisy typing input
consisting of thousands of sentences written by hundreds of users
on QWERTY-layout keyboards. This paper describes the various
subsets contained in this new research dataset as well as the data
format.
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- NSF-PAR ID:
- 10404000
- Date Published:
- Journal Name:
- Companion Proceedings of the 28th International Conference on Intelligent User Interfaces
- Page Range / eLocation ID:
- 251 to 254
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3581754.3584174
