DOI: 10.5281/zenodo.7658801
title: Handling Dynamic Environment Changes for Behavior-Based User Authentication

description: <strong>Description:</strong> This environment-independent user authentication dataset is from our MASS 2020 paper<strong>: <em>Towards Environment-independent Behavior-based User Authentication Using WiFi</em></strong>. This dataset contains the physiological characteristics captured by WiFi from 10 participants for 10 different activities. Each participant performs 20 rounds for each activity. The experiments are conducted in two different environments, the campus office, and the home apartment. The system performance is tested on the cross-environment scenarios (training in one environment and testing in another environment). Note: The MASS 2020 paper is based on our MobiHoc 2017 paper, <strong><em>Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT</em></strong>. The MobiHoc 2017 work focused on user authentication using CSI extracted from human activity while the MASS 2020 work focused on the domain adaptation of user authentication using activity CSI. The dataset of our MobiHoc 2017 work is also published: https://zenodo.org/record/7750976#.ZBfTZ3bMKUk <strong>Format: </strong>.dat format <strong>Section 1: Device Configuration</strong> Two commercial laptops, Dell E6430, as transmitter and receiver. Run with a Linux 14.04 operating system with 4.2.0 kernel. Equipped with 3 MINI PCI-E internal antennas. Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at https://dhalperi.github.io/linux-80211n-csitool/faq.html. WiFi packet transmission is set to 1000 pkts/s <strong>Section 2: Data Format</strong> We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following: 10 participants are included in two different experiments. Each participant performed 20 rounds for each activity. The dataset file name is presented as "User_Day_Action_Location". The detailed information as: User: The participants that CSI was collected from. Day: The date this data was collected. Action: The specific activity performed. Location: The specific location the experiment was conducted. <strong>Section 3: Experimental Setups</strong> There are two experiment setups for our data collection. An image of the experimental setup and the illustration of activities from two different environments is included in the dataset. Each activity was performed in a designated location. In each activity location, the specific activity was conducted in 4 different proximate locations at least one foot away from each other. Residential Apartment Environment: The experiments are conducted in a residential apartment with a size 33ft × 17ft. Participant: 10 users are students from Rutgers University (aged from 20 to 30). Activity: 7 activities were performed. Detailed Activities Performed in Apartment <strong>Code</strong> <strong>Activity</strong> A→B Walking (trajectory 1) B→C Walking (trajectory 2) B Picking up a remote control C Sitting in a chair D Exercising E Operating on the oven F Using the stove Office Environment: The experiments are conducted in an office with a size 21ft × 12ft. Participant: 5 users are students from Rutgers University (aged from 20 to 30). Activity: 3 activities were performed. Detailed Activities Performed in Office <strong>Code</strong> <strong>Activity</strong> G Sitting in a seat H Stretching the body I Typing on a keyboard <strong>Section 4: Data Description</strong> We separate our raw data into different folders based on different environment types. In each environment type, data are further distributed in terms of date. Each file includes all data from three internal antennas. All data files are in .dat format. We also provide Matlab scripts for CSI analysis and visualization. The following variables can be revealed from the codes: CSI: This is the Channel State Information (CSI) received from one receiver antenna. It describes the signal propagation from the transmitter to the receiver, and it is very sensitive to the impact of environmental changes. Each data reveals CSI from 30 subcarriers. Relative Phase: Relative Phase is a measurement to describe the degree of synchronization between data received from different antennas. It can be used to determine the phase offset for further signal preprocessing. Time: This is the time interval in which the data file contains. It measures time by the number of seconds. It can be used to determine how long the signal has been received. <strong>Section 5: Codes</strong> analysis_spectrogram.m: load a .dat file and extract all data by Data description(I.e, CSI, and Relative Phase). <strong>Section 6: Citations</strong> If your paper is related to our works, please cite our papers as follows. https://ieeexplore.ieee.org/document/9356038 C. Shi, J. Liu, N. Borodinov, B. Leao and Y. Chen, "Towards Environment-independent Behavior-based User Authentication Using WiFi," <em>2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)</em>, Delhi, India, 2020, pp. 666-674, doi: 10.1109/MASS50613.2020.00086 <strong>Bibtex:</strong> @INPROCEEDINGS{9356038,<br> author={Shi, Cong and Liu, Jian and Borodinov, Nick and Leao, Bruno and Chen, Yingying},<br> booktitle={2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)}, <br> title={Towards Environment-independent Behavior-based User Authentication Using WiFi}, <br> year={2020},<br> volume={},<br> number={},<br> pages={666-674},<br> doi={10.1109/MASS50613.2020.00086}}<br> The current version of the dataset is shrunk due to its size. If you wish to acquire the full version or you have any questions regarding the dataset, contact us by email: cl1361@scarletmail.rutgers.edu.