Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones
The growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. This paper investigates the feasibility and applicability of using motion-sensor behavior data for user authenticati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/3/345 |
| _version_ | 1818000992140001280 |
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| author | Chao Shen Tianwen Yu Sheng Yuan Yunpeng Li Xiaohong Guan |
| author_facet | Chao Shen Tianwen Yu Sheng Yuan Yunpeng Li Xiaohong Guan |
| author_sort | Chao Shen |
| collection | DOAJ |
| description | The growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. This paper investigates the feasibility and applicability of using motion-sensor behavior data for user authentication on smartphones. For each sample of the passcode, sensory data from motion sensors are analyzed to extract descriptive and intensive features for accurate and fine-grained characterization of users’ passcode-input actions. One-class learning methods are applied to the feature space for performing user authentication. Analyses are conducted using data from 48 participants with 129,621 passcode samples across various operational scenarios and different types of smartphones. Extensive experiments are included to examine the efficacy of the proposed approach, which achieves a false-rejection rate of 6.85% and a false-acceptance rate of 5.01%. Additional experiments on usability with respect to passcode length, sensitivity with respect to training sample size, scalability with respect to number of users, and flexibility with respect to screen size were provided to further explore the effectiveness and practicability. The results suggest that sensory data could provide useful authentication information, and this level of performance approaches sufficiency for two-factor authentication on smartphones. Our dataset is publicly available to facilitate future research. |
| first_indexed | 2024-04-14T03:28:31Z |
| format | Article |
| id | doaj.art-d26bd3ab38a843c48e9e4115bfb74631 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T03:28:31Z |
| publishDate | 2016-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d26bd3ab38a843c48e9e4115bfb746312022-12-22T02:15:04ZengMDPI AGSensors1424-82202016-03-0116334510.3390/s16030345s16030345Performance Analysis of Motion-Sensor Behavior for User Authentication on SmartphonesChao Shen0Tianwen Yu1Sheng Yuan2Yunpeng Li3Xiaohong Guan4School of Electronic and Information Engineering, Xi’an Jiaotong University, 710049 Xi’an, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, 710049 Xi’an, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, 710049 Xi’an, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, 710049 Xi’an, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, 710049 Xi’an, ChinaThe growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. This paper investigates the feasibility and applicability of using motion-sensor behavior data for user authentication on smartphones. For each sample of the passcode, sensory data from motion sensors are analyzed to extract descriptive and intensive features for accurate and fine-grained characterization of users’ passcode-input actions. One-class learning methods are applied to the feature space for performing user authentication. Analyses are conducted using data from 48 participants with 129,621 passcode samples across various operational scenarios and different types of smartphones. Extensive experiments are included to examine the efficacy of the proposed approach, which achieves a false-rejection rate of 6.85% and a false-acceptance rate of 5.01%. Additional experiments on usability with respect to passcode length, sensitivity with respect to training sample size, scalability with respect to number of users, and flexibility with respect to screen size were provided to further explore the effectiveness and practicability. The results suggest that sensory data could provide useful authentication information, and this level of performance approaches sufficiency for two-factor authentication on smartphones. Our dataset is publicly available to facilitate future research.http://www.mdpi.com/1424-8220/16/3/345smartphone securityuser authenticationbehavior analysismotion sensorperformance evaluation |
| spellingShingle | Chao Shen Tianwen Yu Sheng Yuan Yunpeng Li Xiaohong Guan Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones Sensors smartphone security user authentication behavior analysis motion sensor performance evaluation |
| title | Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones |
| title_full | Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones |
| title_fullStr | Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones |
| title_full_unstemmed | Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones |
| title_short | Performance Analysis of Motion-Sensor Behavior for User Authentication on Smartphones |
| title_sort | performance analysis of motion sensor behavior for user authentication on smartphones |
| topic | smartphone security user authentication behavior analysis motion sensor performance evaluation |
| url | http://www.mdpi.com/1424-8220/16/3/345 |
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