Multimodal data for behavioural authentication in Internet of Things environments.

Autor:	Krašovec A; University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, 1000 Ljubljana, Slovenia.; European Commission, Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra (VA), Italy., Baldini G; European Commission, Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra (VA), Italy., Pejović V; University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, 1000 Ljubljana, Slovenia.; Department of Computer Systems, Institute 'Jožef Stefan', Jamova cesta 39, 1000 Ljubljana, Slovenia.
Jazyk:	angličtina
Zdroj:	Data in brief [Data Brief] 2024 Jul 01; Vol. 55, pp. 110697. Date of Electronic Publication: 2024 Jul 01 (Print Publication: 2024).
DOI:	10.1016/j.dib.2024.110697
Abstrakt:	Identifying humans based on their behavioural patterns represents an attractive basis for access control as such patterns appear naturally, do not require a focused effort from the user side, and do not impose the additional burden of memorising passwords. One means of capturing behavioural patterns is through passive sensors laid out in a target environment. Thanks to the proliferation of the Internet of Things (IoT), sensing devices are already embedded in our everyday surroundings and represent a rich source of multimodal data. Nevertheless, collecting such data for authentication research purposes is challenging, as it entails management and synchronisation of a range of sensing devices, design of diverse tasks that would evoke different behaviour patterns, storage and pre-processing of data arriving from multiple sources, and the execution of long-lasting user activities. Consequently, to the best of our knowledge, no publicly available datasets suitable for behaviour-based authentication research exist. In this brief article, we describe the first multimodal dataset for behavioural authentication research collected in a sensor-enabled IoT setting. The dataset comprises of high-frequency accelerometer, gyroscope, and force sensor data collected from an office-like environment. In addition, the dataset contains 3D point clouds collected with wireless radar and electroencephalogram (EEG) readings from a wireless EEG cap worn by the study participants. Within the environment, 54 volunteers conducted 6 different tasks that were constructed to elicit different behaviours and different cognitive load levels, resulting in a total of 16 h of multimodal data. The richness of the dataset comprising 5 different sensing modalities, a variability of tasks including keyboard typing, hand gesturing, walking, and other activities, opens a range of opportunities for research in behaviour-based authentication, but also the understanding of the role of different tasks and cognitive load levels on human behaviour. (© 2024 The Author(s).)
Databáze:	MEDLINE
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