SU-AIS BB-MAS (Syracuse University and Assured Information Security - Behavioral Biometrics Multi-device and multi-Activity data from Same users) Dataset

Name: SU-AIS BB-MAS (Syracuse University and Assured Information Security - Behavioral Biometrics Multi-device and multi-Activity data from Same users) Dataset
Creator: Amith K. Belman
License: https://creativecommons.org/licenses/by/4.0/
Keywords: Artificial Intelligence, Machine Learning, Biophysiological Signals, Sensors, Wearable Sensing, Other

Citation Author(s):: Amith
K. Belman

Syracuse University

Li
Wang
Florida International University

Sundaraja
S. Iyengar
Florida International University

Pawel
Sniatala
Poznan University of Technology

Robert
Wright
Assured Information Secutiy, Inc.

Robert
Dora
Assured Information Secutiy, Inc.

Jacob
Baldwin
Assured Information Secutiy, Inc.

Zhanpeng
Jin
University at Buffalo, SUNY

Vir
V. Phoha

Syracuse University
Submitted by:: Amith Kamath Belman
Last updated:: Tue, 07/21/2020 - 11:18
DOI:: 10.21227/rpaz-0h66
Data Format:: CSV
License:: Creative Commons Attribution

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Categories:: Artificial Intelligence
Machine Learning
Biophysiological Signals
Sensors
Wearable Sensing
Other
Keywords:: Behavioral, Biometrics, Keystrokes, Gait, Swipes, Multi-Device, Multi-Activity, Large-Dataset, Phoha, Belman, Wang, Iyengar, Sniatala, Wright, Dora, Baldwin, Jin

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CITE

Abstract

We provide a large benchmark dataset consisting of about: 3.5 million keystroke events; 57.1 million data-points for accelerometer and gyroscope each; and 1.7 million data-points for swipes. Data was collected between April 2017 and June 2017 after the required IRB approval. Data from 117 participants, in a session lasting between 2 to 2.5 hours each, performing multiple activities such as: typing (free and fixed text), gait (walking, upstairs and downstairs) and swiping activities while using desktop, phone and tablet is shared.

The dataset reported here also contains extracted features which are explored in the literature. The file "Demographics.csv" provides demographics information such as: Age, Height, Handedness, Typing Style, Device usage hours per day and number of Languages spoken and typed for each participant.

Instructions:

Detailed description of all data files is provided in the *BBMAS_README.pdf* file along with the dataset.

Please cite:

[1] Amith K. Belman and Vir V. Phoha. 2020. Discriminative Power of Typing Features on Desktops, Tablets, and Phones for User Identification. ACM Trans. Priv. Secur. Volume 23,Issue 1, Article 4 (February 2020), 36 pages. DOI:https://doi.org/10.1145/3377404

[2]Amith K. Belman, Li Wang, S. S. Iyengar, Pawel Sniatala, Robert Wright, Robert Dora, Jacob Baldwin, Zhanpeng Jin and Vir V. Phoha, "Insights from BB-MAS -- A Large Dataset for Typing, Gait and Swipes of the Same Person on Desktop, Tablet and Phone", arXiv:1912.02736 , 2019.

[3] Amith K. Belman, Li Wang, Sundaraja S. Iyengar, Pawel Sniatala, Robert Wright, Robert Dora, Jacob Baldwin, Zhanpeng Jin, Vir V. Phoha, "SU-AIS BB-MAS (Syracuse University and Assured Information Security - Behavioral Biometrics Multi-device and multi-Activity data from Same users) Dataset ", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/rpaz-0h66

Dataset Files

Dataset zip file. BB-MAS_Dataset.zip (1.49 GB)

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