The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
You Are How You Touch: User Verification on Smartphones via Tapping Behaviors
Abstract
Smartphone users have their own unique behavioral patterns when tapping on the touch screens. These personal patterns are reflected on the different rhythm, strength, and angle preferences of the applied force. Since smart phones are equipped with various sensors like accelerometer, gyroscope, and touch screen sensors, capturing a user's tapping behaviors can be done seamlessly. Exploiting the combination of four features (acceleration, pressure, size, and time) extracted from smart phone sensors, we propose a non-intrusive user verification mechanism to substantiate whether an authenticating user is the true owner of the smart phone or an impostor who happens to know the pass code. Based on the tapping data collected from over 80 users, we conduct a series of experiments to validate the efficacy of our proposed system. Our experimental results show that our verification system achieves high accuracy with averaged equal error rates of down to 3.65%. As our verification system can be seamlessly integrated with the existing user authentication mechanisms on smart phones, its deployment and usage are transparent to users and do not require any extra hardware support.
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Cites
An introduction to biometric recognition
Jain, A. K., Ross, A., & Prabhakar, S. (2004) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2003.818349 · Cited by 4,974
Frank, M., Biedert, R., Ma, E., Martinovic, I., & Song, D. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2225048 · Cited by 844
A Tutorial on Text-Independent Speaker Verification
Bimbot, F., Bonastre, J.-F., Fredouille, C., Gravier, G., Magrin-Chagnolleau, I., Meignier, S., Merlin, T., Ortega-García, J., Petrovska-Delacrétaz, D., & Reynolds, D. A. (2004) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/s1110865704310024 · Cited by 798
Smudge attacks on smartphone touch screens
Aviv, A. J., Gibson, K. L., Mossop, E., Blaze, M., & Smith, J. M. (2010) · Cited by 659
Comparing anomaly-detection algorithms for keystroke dynamics
Killourhy, K. S., & Maxion, R. A. (2009) · 2009 IEEE/IFIP International Conference on Dependable Systems & Networks · doi:10.1109/dsn.2009.5270346 · Cited by 566
User authentication through keystroke dynamics
Bergadano, F., Gunetti, D., & Picardi, C. (2002) · ACM Transactions on Information and System Security · doi:10.1145/581271.581272 · Cited by 513
Authentication via keystroke dynamics
Monrose, F., & Rubin, A. (1997) · Proceedings of the 4th ACM conference on Computer and communications security · doi:10.1145/266420.266434 · Cited by 497
Touch me once and i know it's you!: implicit authentication based on touch screen patterns
De Luca, A., Hang, A., Brudy, F., Lindner, C., & Hussmann, H. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208544 · Cited by 485
ACCessory: password inference using accelerometers on smartphones
Owusu, E., Han, J., Das, S., Perrig, A., & Zhang, J. (2012) · Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications - HotMobile '12 · doi:10.1145/2162081.2162095 · Cited by 370
Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition
Derawi, M. O., Nickel, C., Bours, P., & Busch, C. (2010) · 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing · doi:10.1109/iihmsp.2010.83 · Cited by 343
Cited by
HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users
Sitova, Z., Sedenka, J., Yang, Q., Peng, G., Zhou, G., Gasti, P., & Balagani, K. S. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2506542 · Cited by 398
Zhang (2020) · doi:10.1145/3432192 · Cited by 126
Malik, M. N., Azam, M. A., Ehatisham-Ul-Haq, M., Ejaz, W., & Khalid, A. (2019) · Sensors · doi:10.3390/s19112466 · Cited by 39
Killing the Password and Preserving Privacy With Device-Centric and Attribute-Based Authentication
Papadamou, K., Gevers, S., Xenakis, C., Sirivianos, M., Zannettou, S., Chifor, B., Teican, S., Gugulea, G., Caponi, A., Recupero, A., et al. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2958763 · Cited by 23
Echo-ID: Smart User Identification Leveraging Inaudible Sound Signals
Ali Shah, S. W., Shaghaghi, A., Kanhere, S. S., Zhang, J., Anwar, A., & Doss, R. (2020) · IEEE Access · doi:10.1109/access.2020.3031899 · Cited by 2
Metadata
- Authors
- Zheng, N., Bai, K., Huang, H., & Wang, H.
- Venue
- 2014 IEEE 22nd International Conference on Network Protocols
- Publisher
- IEEE
- Published
- 2014-10
- Pages
- 221-232
- ISSN
- 1092-1648 (ISSN-L)
- OpenAlex
- W2017658349
- Cited by
- 290
- References
- 25
- FWCI
- 30.78
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zheng, N., Bai, K., Huang, H., & Wang, H. (2014). You Are How You Touch: User Verification on Smartphones via Tapping Behaviors. 2014 IEEE 22nd International Conference on Network Protocols. https://doi.org/10.1109/icnp.2014.43
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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