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Multimodal data for behavioural authentication in Internet of Things environments.

Andraž Krašovec1,2, Gianmarco Baldini2, Veljko Pejović1,3

  • 1University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, 1000 Ljubljana, Slovenia.

Data in Brief
|July 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the first multimodal dataset for behavioral authentication research using Internet of Things (IoT) sensors. The dataset captures diverse human behaviors for enhanced access control methods.

Keywords:
Cognitive loadElectroencephalogramInertial measurement unitMulti-modal sensingUbiquitous sensingUser authenticationWireless ranging

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Area of Science:

  • Human-Computer Interaction
  • Biometrics
  • Cybersecurity
  • Internet of Things (IoT)

Background:

  • Behavioral patterns offer a natural and user-friendly basis for access control, unlike traditional password-based methods.
  • Passive sensors within the Internet of Things (IoT) environment can capture rich, multimodal behavioral data.
  • Existing research lacks publicly available datasets suitable for multimodal behavioral authentication studies.

Purpose of the Study:

  • To introduce and describe the first publicly available multimodal dataset for behavioral authentication research.
  • To facilitate research in behavior-based authentication using diverse sensor modalities and user activities.
  • To explore the influence of different tasks and cognitive loads on human behavior patterns.

Main Methods:

  • Collected high-frequency accelerometer, gyroscope, and force sensor data in an office-like IoT setting.
  • Acquired 3D point clouds using wireless radar and electroencephalogram (EEG) readings.
  • Involved 54 participants performing 6 distinct tasks designed to elicit varied behaviors and cognitive loads.

Main Results:

  • Generated a comprehensive multimodal dataset comprising 16 hours of data from 5 different sensing modalities.
  • The dataset includes data from activities such as keyboard typing, hand gesturing, and walking.
  • The collected data exhibits variability in tasks and cognitive load levels, offering rich research opportunities.

Conclusions:

  • The introduced dataset is a valuable resource for advancing behavior-based authentication research in IoT environments.
  • The findings open avenues for developing more robust and natural access control systems.
  • Further research can leverage this dataset to understand the impact of tasks and cognitive states on human behavior.