On using wearable devices to steal your passwords: A fuzzy inference approach

Chao Shen; Ziqiang Ren; Yufei Chen; Zhao Wang

doi:10.1007/978-3-319-69471-9_38

On using wearable devices to steal your passwords: A fuzzy inference approach

Chao Shen
, Ziqiang Ren
, Yufei Chen
, Zhao Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

The security of wearable devices user’s privacy data has become more and more concerned because of the high accuracy of the embedded sensors. Existing methods of obtaining privacy data often rely on installations of dedicated hardware, or accurate numerical calculation of sensor data, which do not have flexible adaptability. In this paper we utilize a multi-SVM and a KNN classifier using only accelerometer data and fuzzy coordinates to get the privacy data such as password directly with a higher accuracy. © 2017, Springer International Publishing AG.

Original language	English
Title of host publication	Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings
Publisher	Springer Verlag
Pages	494-502
Volume	10581 LNCS
ISBN (Print)	9783319694702
DOIs	https://doi.org/10.1007/978-3-319-69471-9_38
Publication status	Published - 2017
Externally published	Yes
Event	9th International Symposium on Cyberspace Safety and Security, CSS 2017 - Xi'an, China Duration: 23 Oct 2017 → 25 Oct 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10581 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Symposium on Cyberspace Safety and Security, CSS 2017
Place	China
City	Xi'an
Period	23/10/17 → 25/10/17

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61403301 and Grant 61773310, in part by the China Postdoctoral Science Foundation under Grant 2014M560783 and Grant 2015T81032, in part by the Natural Science Foundation of Shaanxi Province under Grant 2015JQ6216, and in part by the Fundamental Research Funds for the Central Universities under Grant xjj2015115.

Research Keywords

Motion sensor
Privacy leakage
Side-channel attacks

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10.1007/978-3-319-69471-9_38

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Shen, C., Ren, Z., Chen, Y., & Wang, Z. (2017). On using wearable devices to steal your passwords: A fuzzy inference approach. In Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings (Vol. 10581 LNCS, pp. 494-502). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10581 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-69471-9_38

Shen, Chao ; Ren, Ziqiang ; Chen, Yufei et al. / On using wearable devices to steal your passwords : A fuzzy inference approach. Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings. Vol. 10581 LNCS Springer Verlag, 2017. pp. 494-502 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "On using wearable devices to steal your passwords: A fuzzy inference approach",

abstract = "The security of wearable devices user{\textquoteright}s privacy data has become more and more concerned because of the high accuracy of the embedded sensors. Existing methods of obtaining privacy data often rely on installations of dedicated hardware, or accurate numerical calculation of sensor data, which do not have flexible adaptability. In this paper we utilize a multi-SVM and a KNN classifier using only accelerometer data and fuzzy coordinates to get the privacy data such as password directly with a higher accuracy. {\textcopyright} 2017, Springer International Publishing AG.",

keywords = "Motion sensor, Privacy leakage, Side-channel attacks",

author = "Chao Shen and Ziqiang Ren and Yufei Chen and Zhao Wang",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 9th International Symposium on Cyberspace Safety and Security, CSS 2017 ; Conference date: 23-10-2017 Through 25-10-2017",

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language = "English",

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volume = "10581 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Shen, C, Ren, Z, Chen, Y & Wang, Z 2017, On using wearable devices to steal your passwords: A fuzzy inference approach. in Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings. vol. 10581 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10581 LNCS, Springer Verlag, pp. 494-502, 9th International Symposium on Cyberspace Safety and Security, CSS 2017, Xi'an, China, 23/10/17. https://doi.org/10.1007/978-3-319-69471-9_38

On using wearable devices to steal your passwords: A fuzzy inference approach. / Shen, Chao; Ren, Ziqiang; Chen, Yufei et al.
Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings. Vol. 10581 LNCS Springer Verlag, 2017. p. 494-502 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10581 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - On using wearable devices to steal your passwords

T2 - 9th International Symposium on Cyberspace Safety and Security, CSS 2017

AU - Shen, Chao

AU - Ren, Ziqiang

AU - Chen, Yufei

AU - Wang, Zhao

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2017

Y1 - 2017

N2 - The security of wearable devices user’s privacy data has become more and more concerned because of the high accuracy of the embedded sensors. Existing methods of obtaining privacy data often rely on installations of dedicated hardware, or accurate numerical calculation of sensor data, which do not have flexible adaptability. In this paper we utilize a multi-SVM and a KNN classifier using only accelerometer data and fuzzy coordinates to get the privacy data such as password directly with a higher accuracy. © 2017, Springer International Publishing AG.

AB - The security of wearable devices user’s privacy data has become more and more concerned because of the high accuracy of the embedded sensors. Existing methods of obtaining privacy data often rely on installations of dedicated hardware, or accurate numerical calculation of sensor data, which do not have flexible adaptability. In this paper we utilize a multi-SVM and a KNN classifier using only accelerometer data and fuzzy coordinates to get the privacy data such as password directly with a higher accuracy. © 2017, Springer International Publishing AG.

KW - Motion sensor

KW - Privacy leakage

KW - Side-channel attacks

UR - https://www.scopus.com/pages/publications/85034230600

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85034230600&origin=recordpage

U2 - 10.1007/978-3-319-69471-9_38

DO - 10.1007/978-3-319-69471-9_38

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783319694702

VL - 10581 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 494

EP - 502

BT - Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings

PB - Springer Verlag

Y2 - 23 October 2017 through 25 October 2017

ER -

Shen C, Ren Z, Chen Y, Wang Z. On using wearable devices to steal your passwords: A fuzzy inference approach. In Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings. Vol. 10581 LNCS. Springer Verlag. 2017. p. 494-502. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-69471-9_38

On using wearable devices to steal your passwords: A fuzzy inference approach

Abstract

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Bibliographical note

Funding

Research Keywords

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