TY - GEN
T1 - Generating Datasets Based on the HuMIdb Dataset for Risk-based User Authentication on Smartphones
AU - Papaioannou, Maria
AU - Zachos, Georgios
AU - Mantas, Georgios
AU - Essop, Aliyah
AU - Karasuwa, Abdulkareem
AU - Rodriguez, Jonathan
N1 - Possible OA Compliant version at Greenwich - https://gala.gre.ac.uk/id/eprint/38257/
PY - 2022/12/8
Y1 - 2022/12/8
N2 - User authentication acts as the first line of defense verifying the identity of a mobile user, often as a prerequisite to allow access to resources in a mobile device. Risk-based user authentication based on behavioral biometrics appears to have the potential to increase mobile authentication security without sacrificing usability. Nevertheless, in order to precisely evaluate classification and/or novelty detection algorithms for risk-based user authentication, it is of utmost importance to make use of quality datasets to train and test these algorithms. To the best of our knowledge, there is a lack of up-to-date, representative and comprehensive datasets that are publicly available to the research community for effective training and evaluation of classification and/or novelty detection algorithms suitable for risk-based user authentication. Toward this direction, in this paper, the aim is to provide details on how we generate datasets based on HuMIdb dataset for training and testing classification and novelty detection algorithms for risk-based adaptive user authentication. The HuMIdb dataset is the most recent and publicly available dataset for behavioral user authentication.
AB - User authentication acts as the first line of defense verifying the identity of a mobile user, often as a prerequisite to allow access to resources in a mobile device. Risk-based user authentication based on behavioral biometrics appears to have the potential to increase mobile authentication security without sacrificing usability. Nevertheless, in order to precisely evaluate classification and/or novelty detection algorithms for risk-based user authentication, it is of utmost importance to make use of quality datasets to train and test these algorithms. To the best of our knowledge, there is a lack of up-to-date, representative and comprehensive datasets that are publicly available to the research community for effective training and evaluation of classification and/or novelty detection algorithms suitable for risk-based user authentication. Toward this direction, in this paper, the aim is to provide details on how we generate datasets based on HuMIdb dataset for training and testing classification and novelty detection algorithms for risk-based adaptive user authentication. The HuMIdb dataset is the most recent and publicly available dataset for behavioral user authentication.
KW - dataset generation
KW - mobile device security
KW - record selection
KW - risk-based adaptive user authentication
U2 - 10.1109/CAMAD55695.2022.9966901
DO - 10.1109/CAMAD55695.2022.9966901
M3 - Conference contribution
AN - SCOPUS:85144061953
T3 - IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
SP - 134
EP - 139
BT - 2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022
Y2 - 2 November 2022 through 3 November 2022
ER -