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4 results for “User Authentication”
User Study Data for "HapticLock: Eyes-Free Authentication for Mobile Devices"
<p>User study data from the HapticLock paper published in the Proceedings of the ACM ICMI 2021 conference.</p>
Handling Dynamic Environment Changes for Behavior-Based User Authentication
<p><strong>Description:</strong></p> <p>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).</p> <p>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.</p> <p>The dataset of our MobiHoc 2017 work is also published: <a href="https://zenodo.org/record/7750976#.ZBfTZ3bMKUk">https://zenodo.org/record/7750976#.ZBfTZ3bMKUk</a></p> <p> </p> <p><strong>Format: </strong>.dat format</p> <p><strong>Section 1: Device Configuration</strong></p> <ul> <li>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. </li> <li>Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at <a href="https://dhalperi.github.io/linux-80211n-csitool/faq.html">https://dhalperi.github.io/linux-80211n-csitool/faq.html</a>.</li> <li>WiFi packet transmission is set to 1000 pkts/s</li> </ul> <p><strong>Section 2: Data Format</strong></p> <p>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:</p> <ol> <li>10 participants are included in two different experiments.</li> <li>Each participant performed 20 rounds for each activity.</li> <li>The dataset file name is presented as "User_Day_Action_Location". The detailed information as: <ul> <li>User: The participants that CSI was collected from.</li> <li>Day: The date this data was collected. </li> <li>Action: The specific activity performed.</li> <li>Location: The specific location the experiment was conducted.</li> </ul> </li> </ol> <p><strong>Section 3: Experimental Setups</strong></p> <p>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. </p> <ol> <li>Residential Apartment <ul> <li>Environment: The experiments are conducted in a residential apartment with a size 33ft × 17ft.</li> <li>Participant: 10 users are students from Rutgers University (aged from 20 to 30).</li> <li>Activity: 7 activities were performed. <table> <caption>Detailed Activities Performed in Apartment</caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Activity</strong></td> </tr> <tr> <td> A→B</td> <td>Walking (trajectory 1)</td> </tr> <tr> <td> B→C</td> <td>Walking (trajectory 2)</td> </tr> <tr> <td> B</td> <td>Picking up a remote control</td> </tr> <tr> <td> C</td> <td>Sitting in a chair </td> </tr> <tr> <td> D</td> <td>Exercising</td> </tr> <tr> <td> E</td> <td>Operating on the oven</td> </tr> <tr> <td> F</td> <td>Using the stove</td> </tr> </tbody> </table> <p> </p> </li> </ul> </li> <li>Office <ul> <li>Environment: The experiments are conducted in an office with a size 21ft × 12ft.</li> <li>Participant: 5 users are students from Rutgers University (aged from 20 to 30).</li> <li>Activity: 3 activities were performed. <table> <caption>Detailed Activities Performed in Office</caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Activity</strong></td> </tr> <tr> <td> G</td> <td>Sitting in a seat</td> </tr> <tr> <td> H</td> <td>Stretching the body</td> </tr> <tr> <td> I</td> <td>Typing on a keyboard</td> </tr> </tbody> </table> </li> </ul> </li> </ol> <p> </p> <p><strong>Section 4: Data Description</strong></p> <p>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:</p> <ol> <li>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. </li> <li>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.</li> <li>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.</li> </ol> <p><strong>Section 5: Codes</strong></p> <ul> <li>analysis_spectrogram.m: load a .dat file and extract all data by Data description(I.e, CSI, and Relative Phase).</li> </ul> <p><strong>Section 6: Citations</strong></p> <p>If your paper is related to our works, please cite our papers as follows.</p> <p><a href="https://ieeexplore.ieee.org/document/9356038">https://ieeexplore.ieee.org/document/9356038</a></p> <p>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</p> <p><strong>Bibtex:</strong></p> <p>@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> </p> <p>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. </p>
From Satisfaction to Loyalty: User-perceived Security of Biometric-Based Authentication Methods
<p>While Indonesia has experienced an increase in e-payment systems, phishing attacks that result in the loss of PINs and other sensitive data remain a critical issue for users. Biometric authentication, which identifies individuals based on unique physical traits like fingerprints and facial recognition, is a promising alternative to mitigating these issues. This study explores the factors influencing user satisfaction and continuous use of biometric authentication mechanisms employed in e-payment applications, thereby filling a prominent void in user-centric studies related to this context. Using purposive sampling and structural equation modeling, data were collected from 285 respondents in Indonesia, consisting of biometric-authentication users aged 13 to over 54, between April and July 2024. Eight hypotheses were tested, examining privacy and security risks, system quality, trust, perceived usefulness, self-efficacy, satisfaction, and continuous use. The findings show that trust and perceived usefulness significantly impact satisfaction, which subsequently influences continuous usage. Additionally, system quality and self-efficacy are important factors shaping user perceptions and adoption behavior. The findings from this research provide key insights for e-payment developers, emphasizing the importance of designing systems with strong security, ease of use, and reliable performance to enhance satisfaction and trust. Biometric authentication has the potential to increase public confidence and adoption of biometric technologies, not only in e-payment systems but also in other areas such as e-health and e-commerce. This study lays the groundwork for future research on biometric technologies across broader locations and contexts.</p>
Behavior-based User Authentication Dataset
<p><strong>Description: </strong></p> <p>The behavior-based user authentication dataset is collected from the smart user authentication system through daily activities leveraging commodity WiFi. The dataset contains the extracted CSI features from 8 walking activities and 9 stationary activities from 11 and 5 volunteers, respectively. The experiments are conducted in 2 different environments, including a university office and an apartment. We hope this dataset will help researchers to reproduce the former work of user authentication through WiFi sensing. </p> <p> </p> <p><strong>Dataset Format:</strong></p> <p>.dat files</p> <p><strong>Section 1: Device Configuration: </strong></p> <ul> <li><strong>Transmitter: </strong>Intel 5300 NIC with a Dell E6430 laptop for control. </li> <li><strong>Receiver: </strong>Intel 5300 NIC with a Lenovo T61 laptop for control.</li> <li>Run with a Linux 14.04 operating system with 4.2.0 kernel. Equipped with 3 MINI PCI-E internal antennas. </li> <li>Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at <a href="https://dhalperi.github.io/linux-80211n-csitool/faq.html">https://dhalperi.github.io/linux-80211n-csitool/faq.html</a>.</li> <li><strong>WiFi Packet Rate:</strong> 1000 pkts/s</li> </ul> <p> </p> <p><strong>Section 2: Data Format</strong></p> <p>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:</p> <ol> <li>8 walking activities and 8 stationary activities are collected from 11 and 5 participants are from two different experiments. </li> <li>Each data file contains 30 rounds of one type of activity from each participant.</li> <li>The dataset file name is presented as " Day_Channel_User_Action ". The detailed information as: <ul> <li>Day: The exact date this data was collected. </li> <li>User: The participants that CSI was collected from.</li> <li>Channel: The specific WiFi channel data was collected from.</li> <li>Action: The specific activity performed.</li> </ul> </li> </ol> <p>Note: we select these data specifically to form the dataset to make it efficent, we did not publish every data that we have collected during paper writing. If you have any question regarding the dataset, please contact us for detail information.</p> <p><strong>Section 3: Experimental Setups</strong></p> <p>There are 2 different experiment setups, including a university office and an apartment environment, for our data collection. The detailed setups are shown in the paper. For the activities, we involve 8 walking activities and 8 stationary activities. An image of the experimental setup and the illustration of activities from two different environments is included in the dataset.</p> <ul> <li><strong>Environments: </strong> <ul> <li>2 different environments are involved, including an office environment with the size of 26 ft × 14 ft and an apartment with the size of 36 ft × 22 ft.</li> </ul> </li> <li><strong>Activity description: </strong> <ul> <li>A total of 8 walking activities and 8 stationary activities (30 rounds for each) are performed by 11 and 5 volunteers.</li> <li>The walking activities include 8 different trajectories of walking.</li> <li>The stationary activities include 8 daily activities, such as typing on the keyboard, turning on the light, opening the cabinet, fetching documents, eating, opening the oven, opening the fridge and opening the door.</li> </ul> </li> </ul> <table> <caption>Detailed daily activities performed</caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Walking activity</strong></td> <td><strong>Code</strong></td> <td><strong>Stationary activity</strong></td> </tr> <tr> <td>A</td> <td>Entrance ⇒ Seat</td> <td>a</td> <td>Working (i.e., typing keyboard)</td> </tr> <tr> <td>B</td> <td>Seat ⇒ Entrance</td> <td>b</td> <td>Turning on the light</td> </tr> <tr> <td>C</td> <td>Seat ⇒ Light Switch</td> <td>c</td> <td>Opening the cabinet</td> </tr> <tr> <td>D</td> <td>Light Switch ⇒ Seat</td> <td>d</td> <td>Fetching documents</td> </tr> <tr> <td>E</td> <td>Seat ⇒ Cabinet</td> <td>e</td> <td>Eating at the table</td> </tr> <tr> <td>F</td> <td>Cabinet ⇒ Seat</td> <td>f</td> <td>Opening the microwave oven</td> </tr> <tr> <td>G</td> <td>Entrance ⇒ Kitchen</td> <td>g</td> <td>Opening the refrigerator </td> </tr> <tr> <td>H</td> <td>Kitchen ⇒ Entrance</td> <td>h</td> <td>Opening the door</td> </tr> </tbody> </table> <ul> <li><strong>Number of data samples:</strong> <ul> <li>In total, 3335 activity segments are performed by 11 subjects in the office. 834 activity segments are performed by 5 subjects in the apartment.</li> </ul> </li> </ul> <p> </p> <p><strong>Section 4: Data Description</strong></p> <p>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:</p> <ol> <li>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. </li> <li>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.</li> <li>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.</li> </ol> <p><strong>Section 5: Codes</strong></p> <ul> <li>analysis_spectrogram.m: load a .dat file and extract all data based on the “Data description” (I.e, CSI, and Relative Phase).</li> </ul> <p><strong>Section 6: Citations</strong></p> <p>If your work is related to our work, please cite our papers as follows.</p> <p><a href="https://dl.acm.org/doi/10.1145/3084041.3084061">https://dl.acm.org/doi/10.1145/3084041.3084061</a></p> <p>Cong Shi, Jian Liu, Hongbo Liu, and Yingying Chen. 2017. Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT. In Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing (Mobihoc '17). Association for Computing Machinery, New York, NY, USA, Article 5, 1–10. </p> <p> </p> <p><strong>Bibtex:</strong></p> <p>@inproceedings{shi2017smart,</p> <p> title={Smart user authentication through actuation of daily activities leveraging WiFi-enabled IoT},</p> <p> author={Shi, Cong and Liu, Jian and Liu, Hongbo and Chen, Yingying},</p> <p> booktitle={Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing},</p> <p> pages={1--10},</p> <p> year={2017}</p> <p>}</p>
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