TY - JOUR
T1 - Toward a Secure and Usable User Authentication Mechanism for Mobile Passenger ID Devices for Land/Sea Border Control
AU - Papaioannou, Maria
AU - Zachos, Georgios
AU - Essop, Ismael
AU - Mantas, Georgios
AU - Rodriguez, Jonathan
N1 - Funding Information:
This work was supported by the European Union's Horizon 2020 Research and Innovation Programme under Agreement H2020-MSCA-RISE-2019-eBORDER-872878
Publisher Copyright:
© 2013 IEEE.
PY - 2022/4/15
Y1 - 2022/4/15
N2 - Nowadays the critical sector of transport becomes progressively more dependent on digital technologies to perform essential activities and develop novel efficient transport services and infrastructure to empower economic and social cohesion exploiting the economic strengths of the European Union (EU). However, although the continuously increasing number of visitors, entering the EU through land-border crossing points or seaports, brings immense economic value, novel border control solutions, such as mobile devices for passenger identification for land/sea border control, are essential to precisely identify passengers 'on the fly' ensuring their comfort. Nevertheless, these devices are expected to handle highly confidential personal data and thus, it is very likely to become an attractive target to malicious actors. Therefore, to ensure high level of device security without interrupting border control activities, strong secure and usable user authentication mechanisms are required. Towards this direction, we, firstly, discuss risk-based and adaptive authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge and a novel risk-based adaptive user authentication mechanism is proposed to address this challenge. Afterwards, a set of popular Machine Learning (ML) classification algorithms for risk-based authentication was tested and evaluated on the HuMIdb (Human Mobile Interaction database) dataset to identify the most appropriate ones for the proposed mechanism. The evaluation results demonstrated impact of overfitting (i.e., accuracy: 1,0000) and therefore, we considered novelty detection algorithms to overcome this challenge and demonstrate high performance. To the best of our knowledge, this is the first time that novelty detection algorithms have been considered for risk-based adaptive user authentication showing promising results (OneClassSVM 0,9536, LOF 0,9740, KNN_average 0,9998).
AB - Nowadays the critical sector of transport becomes progressively more dependent on digital technologies to perform essential activities and develop novel efficient transport services and infrastructure to empower economic and social cohesion exploiting the economic strengths of the European Union (EU). However, although the continuously increasing number of visitors, entering the EU through land-border crossing points or seaports, brings immense economic value, novel border control solutions, such as mobile devices for passenger identification for land/sea border control, are essential to precisely identify passengers 'on the fly' ensuring their comfort. Nevertheless, these devices are expected to handle highly confidential personal data and thus, it is very likely to become an attractive target to malicious actors. Therefore, to ensure high level of device security without interrupting border control activities, strong secure and usable user authentication mechanisms are required. Towards this direction, we, firstly, discuss risk-based and adaptive authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge and a novel risk-based adaptive user authentication mechanism is proposed to address this challenge. Afterwards, a set of popular Machine Learning (ML) classification algorithms for risk-based authentication was tested and evaluated on the HuMIdb (Human Mobile Interaction database) dataset to identify the most appropriate ones for the proposed mechanism. The evaluation results demonstrated impact of overfitting (i.e., accuracy: 1,0000) and therefore, we considered novelty detection algorithms to overcome this challenge and demonstrate high performance. To the best of our knowledge, this is the first time that novelty detection algorithms have been considered for risk-based adaptive user authentication showing promising results (OneClassSVM 0,9536, LOF 0,9740, KNN_average 0,9998).
KW - Adaptive user authentication
KW - border control security
KW - mobile passenger ID devices
KW - risk-based user authentication
U2 - 10.1109/ACCESS.2022.3164245
DO - 10.1109/ACCESS.2022.3164245
M3 - Article
AN - SCOPUS:85127515253
SN - 2169-3536
VL - 10
SP - 38832
EP - 38849
JO - IEEE Access
JF - IEEE Access
M1 - 9747935
ER -