Biometric keystroke barcoding: A next-gen authentication framework
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018012" target="_blank" >RIV/62690094:18450/21:50018012 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0957417421004218?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417421004218?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.eswa.2021.114980" target="_blank" >10.1016/j.eswa.2021.114980</a>
Alternative languages
Result language
angličtina
Original language name
Biometric keystroke barcoding: A next-gen authentication framework
Original language description
Investigation of new intelligent solutions for user identification and authentication is and will be essential for enhancing the security of the alphanumeric passwords entered on touchscreen and traditional keyboards. Extraction of the keystrokes has been very beneficial given the intelligent authentication protocols operating in time-domain; while the time-domain solutions drastically lose their efficiency over time due to converging inter-key times. Realistically reflecting the habitual traits, the frequency-domain solutions, however, reveal unique biometric characteristics better, without any risk of convergence. On the contrary, the existing frequency-based frameworks don't provide storable biometric data for further classification of the attempts. Therefore, we propose a novel barcoding framework converting habitual biometric information into storable barcodes as very low-size barcode images. The key-press times are extracted and turned into pseudo-signals exhibiting binary-train characteristics for continuous wavelet transformation (CWT). The transformed signals are primarily categorized with 4-scale scalograms by various complex frequency B-spline wavelets and subsequently superposed to create the unique barcodes. One-class support vector machines (SVM) is employed as the main classifier for training and testing the barcodes and very promising results are achieved given the lowest equal error rate (EER) of 1.83%. © 2021 Elsevier Ltd
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Expert systems with applications
ISSN
0957-4174
e-ISSN
—
Volume of the periodical
177
Issue of the periodical within the volume
September
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
"Article number 114980"
UT code for WoS article
000663300400004
EID of the result in the Scopus database
2-s2.0-85103945632