WearAuth: Wristwear-Assisted User Authentication for Smartphones Using Wavelet-Based Multi-Resolution Analysis
Zero-effort bilateral authentication was introduced recently to use a trusted wristwear to continuously authenticate a smartphone user. A user is allowed to use the smartphone if both wristwear and smartphone are determined to be held by the same person by comparing the wristwear's motion with...
Saved in:
| Published in | IEICE Transactions on Information and Systems Vol. E102.D; no. 10; pp. 1976 - 1992 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Tokyo
The Institute of Electronics, Information and Communication Engineers
01.10.2019
Japan Science and Technology Agency |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0916-8532 1745-1361 1745-1361 |
| DOI | 10.1587/transinf.2019EDP7024 |
Cover
| Abstract | Zero-effort bilateral authentication was introduced recently to use a trusted wristwear to continuously authenticate a smartphone user. A user is allowed to use the smartphone if both wristwear and smartphone are determined to be held by the same person by comparing the wristwear's motion with the smartphone's input or motion, depending on the grip — which hand holds the smartphone and which hand provides the input. Unfortunately, the scheme has several shortcomings. First, it may work improperly when the user is walking since the gait can conceal the wrist's motions of making touches. Second, it continuously compares the motions of the two devices, which incurs a heavy communication burden. Third, the acceleration-based grip inference, which assumes that the smartphone is horizontal with the ground is inapplicable in practice. To address these shortcomings, we propose WearAuth, wristwear-assisted user authentication for smartphones in this paper. WearAuth applies wavelet-based multi-resolution analysis to extract the desired touch-specific movements regardless of whether the user is stationary or moving; uses discrete Fourier transform-based approximate correlation to reduce the communication overhead; and takes a new approach to directly compute the relative device orientation without using acceleration to infer the grip more precisely. In two experiments with 50 subjects, WearAuth produced false negative rates of 3.6% or less and false positive rates of 1.69% or less. We conclude that WearAuth operates properly under various usage cases and is robust to sophisticated attacks. |
|---|---|
| AbstractList | Zero-effort bilateral authentication was introduced recently to use a trusted wristwear to continuously authenticate a smartphone user. A user is allowed to use the smartphone if both wristwear and smartphone are determined to be held by the same person by comparing the wristwear's motion with the smartphone's input or motion, depending on the grip — which hand holds the smartphone and which hand provides the input. Unfortunately, the scheme has several shortcomings. First, it may work improperly when the user is walking since the gait can conceal the wrist's motions of making touches. Second, it continuously compares the motions of the two devices, which incurs a heavy communication burden. Third, the acceleration-based grip inference, which assumes that the smartphone is horizontal with the ground is inapplicable in practice. To address these shortcomings, we propose WearAuth, wristwear-assisted user authentication for smartphones in this paper. WearAuth applies wavelet-based multi-resolution analysis to extract the desired touch-specific movements regardless of whether the user is stationary or moving; uses discrete Fourier transform-based approximate correlation to reduce the communication overhead; and takes a new approach to directly compute the relative device orientation without using acceleration to infer the grip more precisely. In two experiments with 50 subjects, WearAuth produced false negative rates of 3.6% or less and false positive rates of 1.69% or less. We conclude that WearAuth operates properly under various usage cases and is robust to sophisticated attacks. |
| Author | KANG, Taeho JI, Sangwoo KIM, Jong ZHU, Bin JEONG, Hayoung |
| Author_xml | – sequence: 1 fullname: ZHU, Bin organization: Microsoft Research Asia – sequence: 1 fullname: JI, Sangwoo organization: Pohang University of Science and Technology (POSTECH) – sequence: 1 fullname: JEONG, Hayoung organization: Pohang University of Science and Technology (POSTECH) – sequence: 1 fullname: KANG, Taeho organization: Pohang University of Science and Technology (POSTECH) – sequence: 1 fullname: KIM, Jong organization: Pohang University of Science and Technology (POSTECH) |
| BookMark | eNp9kUtv2zAQhInAAeo8_kEOAnpWyqVISczNtd0HkCBBHvCRWFOULUOlVJJq4H9fqnbTnHraXXC-wWB4Ria2s4aQK6DXIMriU3BofWPra0ZBLhcPBWX8hEyh4CKFLIcJmVIJeVqKjH0gZ97vKIWSgZgSuzLoZkPY3iQr1_jwGs905n1cTZW8eOOS8dXY0GgMTWeTunPJ0w90od_GFD5qGrtJVvjLtCakn9FH7m5oQ5M-Gt-1wx9oZrHdR9MLclpj683lcZ6Tly_L5_m39Pb-6_f57DbVAoqQwlobXa4ll1rnlUYqMWc5lJmuQSOXlNVlVnGJhazWAmUJlZaa1zUXa6hElZ0TcfAdbI_7V2xb1bsmpt4roGosTf0tTY2lmaofS4vcxwPXu-7nYHxQu25wMbxXjElZMp7nMqr4QaVd570z9f_Njz8SsacDtvMBN-YNil02ujX_oCVQphaj2XF75_Km1lt0ytjsNypOoqM |
| Cites_doi | 10.1109/INFOCOM.2017.8057145 10.1007/978-0-387-84858-7 10.1109/DSN.2017.21 10.1109/TMC.2009.51 10.1007/978-3-540-74853-3_18 10.1145/1814433.1814466 10.1145/3023954 10.1109/ICASSP.2012.6289074 10.1145/1807167.1807188 10.1109/TDSC.2015.2427848 10.14722/ndss.2016.23199 10.1145/2702123.2702252 10.1109/TNSRE.2013.2265887 10.1023/A:1010933404324 10.1145/2976749.2978294 10.1137/1.9781611970104 10.1080/01621459.1997.10474042 10.1017/CBO9780511801389 10.1006/jcss.1997.1504 10.1109/SP.2014.51 10.1589/jpts.20.243 10.1145/2948618.2948627 10.1093/biomet/82.3.619 10.1109/TMC.2014.2365185 10.1145/2660267.2660334 |
| ContentType | Journal Article |
| Copyright | 2019 The Institute of Electronics, Information and Communication Engineers Copyright Japan Science and Technology Agency 2019 |
| Copyright_xml | – notice: 2019 The Institute of Electronics, Information and Communication Engineers – notice: Copyright Japan Science and Technology Agency 2019 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D ADTOC UNPAY |
| DOI | 10.1587/transinf.2019EDP7024 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1745-1361 |
| EndPage | 1992 |
| ExternalDocumentID | 10.1587/transinf.2019edp7024 10_1587_transinf_2019EDP7024 article_transinf_E102_D_10_E102_D_2019EDP7024_article_char_en |
| GroupedDBID | -~X 5GY ABJNI ABZEH ACGFS ADNWM AENEX ALMA_UNASSIGNED_HOLDINGS CS3 DU5 EBS EJD F5P ICE JSF JSH KQ8 OK1 P2P RJT RZJ TN5 ZKX AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D 1TH ADTOC AFFNX C1A CKLRP H13 RYL UNPAY VOH ZE2 ZY4 |
| ID | FETCH-LOGICAL-c517t-1bcec8b949cc6dca09a626183cf1ca4902f83d49a79db5a981dc9c4ff45b1d5d3 |
| IEDL.DBID | UNPAY |
| ISSN | 0916-8532 1745-1361 |
| IngestDate | Wed Oct 01 16:09:33 EDT 2025 Mon Jun 30 05:31:16 EDT 2025 Wed Oct 01 03:08:02 EDT 2025 Wed Sep 03 06:22:40 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c517t-1bcec8b949cc6dca09a626183cf1ca4902f83d49a79db5a981dc9c4ff45b1d5d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://www.jstage.jst.go.jp/article/transinf/E102.D/10/E102.D_2019EDP7024/_pdf |
| PQID | 2299824669 |
| PQPubID | 2048497 |
| PageCount | 17 |
| ParticipantIDs | unpaywall_primary_10_1587_transinf_2019edp7024 proquest_journals_2299824669 crossref_primary_10_1587_transinf_2019EDP7024 jstage_primary_article_transinf_E102_D_10_E102_D_2019EDP7024_article_char_en |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2019-10-01 |
| PublicationDateYYYYMMDD | 2019-10-01 |
| PublicationDate_xml | – month: 10 year: 2019 text: 2019-10-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Tokyo |
| PublicationPlace_xml | – name: Tokyo |
| PublicationTitle | IEICE Transactions on Information and Systems |
| PublicationTitleAlternate | IEICE Trans. Inf. & Syst. |
| PublicationYear | 2019 |
| Publisher | The Institute of Electronics, Information and Communication Engineers Japan Science and Technology Agency |
| Publisher_xml | – name: The Institute of Electronics, Information and Communication Engineers – name: Japan Science and Technology Agency |
| References | [17] NTP: The Network Time Protocol, http://www.ntp.org/. [14] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods,Cambridge University Press, New York, NY, USA, 2000. [35] W.-H. Lee and R. Lee, “Implicit sensor-based authentication of smartphone users with smartwatch,” Proc. Hardware and Architectural Support for Security and Privacy 2016, pp.9:1-9:8, 2016. 10.1145/2948618.2948627 [6] D.B. Percival and A.T. Walden, Wavelet Methods for Time Series Analysis, Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press, 2000. [9] O. Huhta, P. Shrestha, S. Udar, M. Juuti, N. Saxena, and N. Asokan, “Pitfalls in designing zero-effort deauthentication: Opportunistic human observation attacks,” The Network and Distributed System Security Symposium, 2016. [2] S. Mare, A.M. Markham, C. Cornelius, R. Peterson, and D. Kotz, “ZEBRA: Zero-effort bilateral recurring authentication,” IEEE Symposium on Security and Privacy, pp.705-720, May 2014. 10.1109/sp.2014.51 [8] I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, 1992. [26] Google: Smart lock, https://get.google.com/smartlock/. [15] L. Breiman, “Random forests,” Machine Learning, vol.45, no.1, pp.5-32, Oct. 2001. 10.1023/a:1010933404324 [29] R. Mayrhofer and H. Gellersen, “Shake well before use: Intuitive and secure pairing of mobile devices,” IEEE Trans. Mobile Comput., vol.8, no.6, pp.792-806, June 2009. 10.1109/tmc.2009.51 [11] M. Kojima, S. Obuchi, O. Henmi, and N. Ikeda, “Comparison of Smoothness during Gait between Community Dwelling Elderly Fallers and Non-Fallers Using Power Spectrum Entropy of Acceleration Time-Series,” Journal of Physical Therapy Science, vol.20, no.4, pp.243-248, 2008. 10.1589/jpts.20.243 [25] L. Li, X. Zhao, and G. Xue, “Unobservable re-authentication for smartphones,” The Network and Distributed System Security Symposium, 2013. [7] S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, 2009. [19] A. Ivanov and G. Riccardi, “Kolmogorov-smirnov test for feature selection in emotion recognition from speech,” 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5125-5128, March 2012. 10.1109/icassp.2012.6289074 [32] T. Li, Y. Chen, J. Sun, X. Jin, and Y. Zhang, “iLock: Immediate and automatic locking of mobile devices against data theft,” Proc. ACM SIGSAC Conference on Computer and Communications Security, pp.933-944, 2016. 10.1145/2976749.2978294 [1] S. Mare, “Seamless Authentication for Ubiquitous Devices,” Tech. Rep. TR2016-793, Dartmouth College, Computer Science, Hanover, NH, May 2016. [31] N. Karapanos, C. Marforio, C. Soriente, and S. Čapkun, “Sound-proof: Usable two-factor authentication based on ambient sound,” 24th USENIX Security Symposium, pp.483-498, 2015. [30] L. Li, X. Zhao, and G. Xue, “A proximity authentication system for smartphones,” IEEE Transactions on Dependable and Secure Computing, vol.13, no.6, pp.605-616, Nov. 2016. 10.1109/tdsc.2015.2427848 [10] E. Sejdić, K.A. Lowry, J. Bellanca, M.S. Redfern, and J.S. Brach, “A Comprehensive Assessment of Gait Accelerometry Signals in Time, Frequency and Time-Frequency Domains,” IEEE Trans. Neural Syst. Rehabil. Eng., vol.22, no.3, pp.603-612, 2014. 10.1109/tnsre.2013.2265887 [18] D. Bichler, G. Stromberg, M. Huemer, and M. Löw, “Key generation based on acceleration data of shaking processes,” Proc. 9th International Conference of Ubiquitous Computing, pp.304-317, 2007. [28] W. Xu, C. Javali, G. Revadigar, C. Luo, N. Bergmann, and W. Hu, “Gait-key: A gait-based shared secret key generation protocol for wearable devices,” ACM Trans. Sen. Netw., vol.13, no.1, pp.6:1-6:27, Jan. 2017. 10.1145/3023954 [13] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer-Verlag, New York, 2009. [24] D. Buschek, A. De Luca, and F. Alt, “Improving accuracy, applicability and usability of keystroke biometrics on mobile touchscreen devices,” Proc. 33rd Annual ACM Conference on Human Factors in Computing Systems, pp.1393-1402, 2015. 10.1145/2702123.2702252 [34] M. Miettinen, N. Asokan, T.D. Nguyen, A.-R. Sadeghi, and M.Sobhani, “Context-based zero-interaction pairing and key evolution for advanced personal devices,” Proc. ACM SIGSAC Conference on Computer and Communications Security, pp.880-891, 2014. 10.1145/2660267.2660334 [21] “How Do Users Really Hold Mobile Devices,” https://www.uxmatters.com/mt/archives/2013/02/how-do-users-really-hold-mobile-devices.php. [16] Y. Freund and R.E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol.55, no.1, pp.119-139, 1997. 10.1006/jcss.1997.1504 [33] A. Kalamandeen, A. Scannell, E. de Lara, A. Sheth, and A. LaMarca, “Ensemble: Cooperative proximity-based authentication,” Proc. 8th International Conference on Mobile Systems, Applications, and Services, pp.331-344, 2010. 10.1145/1814433.1814466 [22] J. Zhang, Z. Wang, Z. Yang, and Q. Zhang, “Proximity based iot device authentication,” IEEE INFOCOM 2017-IEEE Conference on Computer Communications, pp.1-9, May 2017. 10.1109/infocom.2017.8057145 [23] Y. Ren, Y. Chen, M.C. Chuah, and J. Yang, “User verification leveraging gait recognition for smartphone enabled mobile healthcare systems,” IEEE Trans. Mobile Comput., vol.14, no.9, pp.1961-1974, Sept. 2015. 10.1109/tmc.2014.2365185 [12] L. Breiman, J. Friedman, C. Stone, and R. Olshen, Classification and Regression Trees, The Wadsworth and Brooks-Cole statistics-probability series, Taylor & Francis, 1984. [36] W.-H. Lee and R.B. Lee, “Sensor-based implicit authentication of smartphone users,” 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp.309-320, June 2017. 10.1109/dsn.2017.21 [3] D.P. Percival, “On estimation of the wavelet variance,” Biometrika, vol.82, no.3, pp.619-631, 1995. 10.1093/biomet/82.3.619 [5] A. Mueen, S. Nath, and J. Liu, “Fast approximate correlation for massive time-series data,” Proc. 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD '10, pp.171-182, 2010. 10.1145/1807167.1807188 [20] MathWorks, MATLAB, https://www.mathworks.com/products/matlab.html. [4] D.B. Percival and H.O. Mofjeld, “Analysis of subtidal coastal sea level fluctuations using wavelets,” Journal of the American Statistical Association, vol.92, no.439, pp.868-880, 1997. 10.1080/01621459.1997.10474042 [27] “How to unlock your mac with your apple watch,” https://support.apple.com/en-us/HT206995. 22 23 24 25 26 27 28 29 30 31 10 32 11 33 12 34 13 35 14 36 15 16 17 18 19 1 2 3 4 5 6 7 8 9 20 21 |
| References_xml | – reference: [35] W.-H. Lee and R. Lee, “Implicit sensor-based authentication of smartphone users with smartwatch,” Proc. Hardware and Architectural Support for Security and Privacy 2016, pp.9:1-9:8, 2016. 10.1145/2948618.2948627 – reference: [1] S. Mare, “Seamless Authentication for Ubiquitous Devices,” Tech. Rep. TR2016-793, Dartmouth College, Computer Science, Hanover, NH, May 2016. – reference: [6] D.B. Percival and A.T. Walden, Wavelet Methods for Time Series Analysis, Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press, 2000. – reference: [32] T. Li, Y. Chen, J. Sun, X. Jin, and Y. Zhang, “iLock: Immediate and automatic locking of mobile devices against data theft,” Proc. ACM SIGSAC Conference on Computer and Communications Security, pp.933-944, 2016. 10.1145/2976749.2978294 – reference: [30] L. Li, X. Zhao, and G. Xue, “A proximity authentication system for smartphones,” IEEE Transactions on Dependable and Secure Computing, vol.13, no.6, pp.605-616, Nov. 2016. 10.1109/tdsc.2015.2427848 – reference: [14] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods,Cambridge University Press, New York, NY, USA, 2000. – reference: [33] A. Kalamandeen, A. Scannell, E. de Lara, A. Sheth, and A. LaMarca, “Ensemble: Cooperative proximity-based authentication,” Proc. 8th International Conference on Mobile Systems, Applications, and Services, pp.331-344, 2010. 10.1145/1814433.1814466 – reference: [8] I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, 1992. – reference: [13] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer-Verlag, New York, 2009. – reference: [17] NTP: The Network Time Protocol, http://www.ntp.org/. – reference: [27] “How to unlock your mac with your apple watch,” https://support.apple.com/en-us/HT206995. – reference: [9] O. Huhta, P. Shrestha, S. Udar, M. Juuti, N. Saxena, and N. Asokan, “Pitfalls in designing zero-effort deauthentication: Opportunistic human observation attacks,” The Network and Distributed System Security Symposium, 2016. – reference: [11] M. Kojima, S. Obuchi, O. Henmi, and N. Ikeda, “Comparison of Smoothness during Gait between Community Dwelling Elderly Fallers and Non-Fallers Using Power Spectrum Entropy of Acceleration Time-Series,” Journal of Physical Therapy Science, vol.20, no.4, pp.243-248, 2008. 10.1589/jpts.20.243 – reference: [25] L. Li, X. Zhao, and G. Xue, “Unobservable re-authentication for smartphones,” The Network and Distributed System Security Symposium, 2013. – reference: [34] M. Miettinen, N. Asokan, T.D. Nguyen, A.-R. Sadeghi, and M.Sobhani, “Context-based zero-interaction pairing and key evolution for advanced personal devices,” Proc. ACM SIGSAC Conference on Computer and Communications Security, pp.880-891, 2014. 10.1145/2660267.2660334 – reference: [24] D. Buschek, A. De Luca, and F. Alt, “Improving accuracy, applicability and usability of keystroke biometrics on mobile touchscreen devices,” Proc. 33rd Annual ACM Conference on Human Factors in Computing Systems, pp.1393-1402, 2015. 10.1145/2702123.2702252 – reference: [20] MathWorks, MATLAB, https://www.mathworks.com/products/matlab.html. – reference: [16] Y. Freund and R.E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol.55, no.1, pp.119-139, 1997. 10.1006/jcss.1997.1504 – reference: [31] N. Karapanos, C. Marforio, C. Soriente, and S. Čapkun, “Sound-proof: Usable two-factor authentication based on ambient sound,” 24th USENIX Security Symposium, pp.483-498, 2015. – reference: [36] W.-H. Lee and R.B. Lee, “Sensor-based implicit authentication of smartphone users,” 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp.309-320, June 2017. 10.1109/dsn.2017.21 – reference: [3] D.P. Percival, “On estimation of the wavelet variance,” Biometrika, vol.82, no.3, pp.619-631, 1995. 10.1093/biomet/82.3.619 – reference: [29] R. Mayrhofer and H. Gellersen, “Shake well before use: Intuitive and secure pairing of mobile devices,” IEEE Trans. Mobile Comput., vol.8, no.6, pp.792-806, June 2009. 10.1109/tmc.2009.51 – reference: [19] A. Ivanov and G. Riccardi, “Kolmogorov-smirnov test for feature selection in emotion recognition from speech,” 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5125-5128, March 2012. 10.1109/icassp.2012.6289074 – reference: [2] S. Mare, A.M. Markham, C. Cornelius, R. Peterson, and D. Kotz, “ZEBRA: Zero-effort bilateral recurring authentication,” IEEE Symposium on Security and Privacy, pp.705-720, May 2014. 10.1109/sp.2014.51 – reference: [7] S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, 2009. – reference: [23] Y. Ren, Y. Chen, M.C. Chuah, and J. Yang, “User verification leveraging gait recognition for smartphone enabled mobile healthcare systems,” IEEE Trans. Mobile Comput., vol.14, no.9, pp.1961-1974, Sept. 2015. 10.1109/tmc.2014.2365185 – reference: [15] L. Breiman, “Random forests,” Machine Learning, vol.45, no.1, pp.5-32, Oct. 2001. 10.1023/a:1010933404324 – reference: [18] D. Bichler, G. Stromberg, M. Huemer, and M. Löw, “Key generation based on acceleration data of shaking processes,” Proc. 9th International Conference of Ubiquitous Computing, pp.304-317, 2007. – reference: [4] D.B. Percival and H.O. Mofjeld, “Analysis of subtidal coastal sea level fluctuations using wavelets,” Journal of the American Statistical Association, vol.92, no.439, pp.868-880, 1997. 10.1080/01621459.1997.10474042 – reference: [22] J. Zhang, Z. Wang, Z. Yang, and Q. Zhang, “Proximity based iot device authentication,” IEEE INFOCOM 2017-IEEE Conference on Computer Communications, pp.1-9, May 2017. 10.1109/infocom.2017.8057145 – reference: [26] Google: Smart lock, https://get.google.com/smartlock/. – reference: [21] “How Do Users Really Hold Mobile Devices,” https://www.uxmatters.com/mt/archives/2013/02/how-do-users-really-hold-mobile-devices.php. – reference: [28] W. Xu, C. Javali, G. Revadigar, C. Luo, N. Bergmann, and W. Hu, “Gait-key: A gait-based shared secret key generation protocol for wearable devices,” ACM Trans. Sen. Netw., vol.13, no.1, pp.6:1-6:27, Jan. 2017. 10.1145/3023954 – reference: [5] A. Mueen, S. Nath, and J. Liu, “Fast approximate correlation for massive time-series data,” Proc. 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD '10, pp.171-182, 2010. 10.1145/1807167.1807188 – reference: [12] L. Breiman, J. Friedman, C. Stone, and R. Olshen, Classification and Regression Trees, The Wadsworth and Brooks-Cole statistics-probability series, Taylor & Francis, 1984. – reference: [10] E. Sejdić, K.A. Lowry, J. Bellanca, M.S. Redfern, and J.S. Brach, “A Comprehensive Assessment of Gait Accelerometry Signals in Time, Frequency and Time-Frequency Domains,” IEEE Trans. Neural Syst. Rehabil. Eng., vol.22, no.3, pp.603-612, 2014. 10.1109/tnsre.2013.2265887 – ident: 22 doi: 10.1109/INFOCOM.2017.8057145 – ident: 13 doi: 10.1007/978-0-387-84858-7 – ident: 36 doi: 10.1109/DSN.2017.21 – ident: 12 – ident: 29 doi: 10.1109/TMC.2009.51 – ident: 18 doi: 10.1007/978-3-540-74853-3_18 – ident: 31 – ident: 33 doi: 10.1145/1814433.1814466 – ident: 28 doi: 10.1145/3023954 – ident: 19 doi: 10.1109/ICASSP.2012.6289074 – ident: 5 doi: 10.1145/1807167.1807188 – ident: 7 – ident: 30 doi: 10.1109/TDSC.2015.2427848 – ident: 9 doi: 10.14722/ndss.2016.23199 – ident: 20 – ident: 26 – ident: 17 – ident: 24 doi: 10.1145/2702123.2702252 – ident: 10 doi: 10.1109/TNSRE.2013.2265887 – ident: 15 doi: 10.1023/A:1010933404324 – ident: 1 – ident: 32 doi: 10.1145/2976749.2978294 – ident: 8 doi: 10.1137/1.9781611970104 – ident: 4 doi: 10.1080/01621459.1997.10474042 – ident: 14 doi: 10.1017/CBO9780511801389 – ident: 16 doi: 10.1006/jcss.1997.1504 – ident: 2 doi: 10.1109/SP.2014.51 – ident: 11 doi: 10.1589/jpts.20.243 – ident: 35 doi: 10.1145/2948618.2948627 – ident: 3 doi: 10.1093/biomet/82.3.619 – ident: 6 – ident: 23 doi: 10.1109/TMC.2014.2365185 – ident: 21 – ident: 34 doi: 10.1145/2660267.2660334 – ident: 27 – ident: 25 |
| SSID | ssj0018215 |
| Score | 2.1928627 |
| Snippet | Zero-effort bilateral authentication was introduced recently to use a trusted wristwear to continuously authenticate a smartphone user. A user is allowed to... |
| SourceID | unpaywall proquest crossref jstage |
| SourceType | Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 1976 |
| SubjectTerms | Acceleration Fourier transforms Gait motion sensor signal processing smart devices Smartphones user authentication Wavelet analysis Wrist |
| Title | WearAuth: Wristwear-Assisted User Authentication for Smartphones Using Wavelet-Based Multi-Resolution Analysis |
| URI | https://www.jstage.jst.go.jp/article/transinf/E102.D/10/E102.D_2019EDP7024/_article/-char/en https://www.proquest.com/docview/2299824669 https://www.jstage.jst.go.jp/article/transinf/E102.D/10/E102.D_2019EDP7024/_pdf |
| UnpaywallVersion | publishedVersion |
| Volume | E102.D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | IEICE Transactions on Information and Systems, 2019/10/01, Vol.E102.D(10), pp.1976-1992 |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1745-1361 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0018215 issn: 0916-8532 databaseCode: KQ8 dateStart: 20080101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB61WyTgQKGAWCiVD1ydTRzbibkV2qoCVIpgteUU-ZVKpSwRzaqCX8947SxbDkhInJIotjWx5_GNMzMGeFFz5hi6x9T7llP0NxjVrcnRVZHc2rYopVkGyJ7I4yl_cybONuD9kAsTwiovEBed-3DJzr9lF90kTeKkD-obJ35yiGYxO0CJT3cNmjFEj6cVWpxJ07l2E7akQHA-gq3pyen-52XFvUJSNE4spkgKinQUKZlO1NVq8BDupbzrwlg3jNWtSNcNKHp7Me_0j2t9eblmlY62oRu-JwajfMkWvcnszz9KPf7HD74P9xKCJfux8wPY8PMd2B5OhyBJWezA3bVShw9hPkORCvtxL8ks6JVrfKTIG4HLHJmiIJDwNsQuxU1EgmiafPyKNIboeX9FlsENZKbDSRk9fYXW15Fl-jANvyCiAJGhxsojmB4dfnp9TNNZD9SKouppYay3tVFcWSud1bnS6GqhvkF-sZqrnLV16bjSlXJGaIUw2yrL25YLUzjhyscwmiM1T4BolgutS1d5W3JtrGE4lpVKlywv29qPgQ6L2nSxpEcTXCFkgmaY8PX5HcO7uECr1ml5frcOi9IchFHS3Vr3VeuQRoe6aAy7A_80SV9cNQxRQc24lGoM2Yqn_k5e4tGn_9rhGdwJTzEicRdG_feFf47Iqjd7sPn2Q72XZOYXKqYlag |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB6VLRL00EIBsbQgH7g6mzi2E3MrtFWFUKkEqy2nyK9UKmWJaFZV-fWM186y5YCExCmJYlsTex7fODNjgNc1Z46he0y9bzlFf4NR3ZocXRXJrW2LUpplgOypPJny9-fifAM-DrkwIazyEnHRhQ-X7OJ7dtlN0iRO-qC-ceInR2gWs0OU-HTXoBlD9HhWocWZNJ1r78GmFAjOR7A5PT07-LKsuFdIisaJxRRJQZGOIiXTibpaDR7CvZR3XRjrjrG6H-m6A0UfLOadvr3RV1drVul4B7rhe2Iwytds0ZvM_vyj1ON__OBHsJ0QLDmInR_Dhp_vws5wOgRJymIXttZKHT6B-QxFKuzHvSGzoFdu8JEibwQuc2SKgkDC2xC7FDcRCaJp8ukb0hii5_01WQY3kJkOJ2X09C1aX0eW6cM0_IKIAkSGGitPYXp89PndCU1nPVAriqqnhbHe1kZxZa10VudKo6uF-gb5xWquctbWpeNKV8oZoRXCbKssb1suTOGEK5_BaI7UPAeiWS60Ll3lbcm1sYbhWFYqXbK8bGs_BjosatPFkh5NcIWQCZphwtfndwwf4gKtWqfl-d06LEpzGEZJd2vdV61DGh3qojHsD_zTJH1x3TBEBTXjUqoxZCue-jt5iUdf_GuHPXgYnmJE4j6M-h8L_xKRVW9eJWn5BU_WJHU |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=WearAuth%3A+Wristwear-Assisted+User+Authentication+for+Smartphones+Using+Wavelet-Based+Multi-Resolution+Analysis&rft.jtitle=IEICE+Transactions+on+Information+and+Systems&rft.au=ZHU%2C+Bin&rft.au=JI%2C+Sangwoo&rft.au=JEONG%2C+Hayoung&rft.au=KANG%2C+Taeho&rft.date=2019-10-01&rft.pub=The+Institute+of+Electronics%2C+Information+and+Communication+Engineers&rft.issn=0916-8532&rft.eissn=1745-1361&rft.volume=E102.D&rft.issue=10&rft.spage=1976&rft.epage=1992&rft_id=info:doi/10.1587%2Ftransinf.2019EDP7024&rft.externalDocID=article_transinf_E102_D_10_E102_D_2019EDP7024_article_char_en |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0916-8532&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0916-8532&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0916-8532&client=summon |