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

^*Corresponding author for this work

Xi'an Jiaotong University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › 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.

Original language	English
Title of host publication	Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings
Editors	Wei Wu, Aniello Castiglione, Sheng Wen
Publisher	Springer Verlag
Pages	494-502
Number of pages	9
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
Country/Territory	China
City	Xi'an
Period	23/10/17 → 25/10/17

Keywords

Motion sensor
Privacy leakage
Side-channel attacks

Access to Document

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

Cite this

Shen, C., Ren, Z., Chen, Y., & Wang, Z. (2017). On using wearable devices to steal your passwords: A fuzzy inference approach. In W. Wu, A. Castiglione, & S. Wen (Eds.), Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings (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. editor / Wei Wu ; Aniello Castiglione ; Sheng Wen. Springer Verlag, 2017. pp. 494-502 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5b254a59cb0541fc8849e8eb91138a8d,

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.",

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

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

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 9th International Symposium on Cyberspace Safety and Security, CSS 2017 ; Conference date: 23-10-2017 Through 25-10-2017",

year = "2017",

doi = "10.1007/978-3-319-69471-9_38",

language = "English",

isbn = "9783319694702",

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

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editor = "Wei Wu and Aniello Castiglione and Sheng Wen",

booktitle = "Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings",

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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 W Wu, A Castiglione & S Wen (eds), Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings. 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. ed. / Wei Wu; Aniello Castiglione; Sheng Wen. 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: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Shen, Chao

AU - Ren, Ziqiang

AU - Chen, Yufei

AU - Wang, Zhao

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.

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.

KW - Motion sensor

KW - Privacy leakage

KW - Side-channel attacks

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

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

M3 - Conference Proceeding

AN - SCOPUS:85034230600

SN - 9783319694702

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

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

A2 - Wu, Wei

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PB - Springer Verlag

Y2 - 23 October 2017 through 25 October 2017

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Shen C, Ren Z, Chen Y, Wang Z. On using wearable devices to steal your passwords: A fuzzy inference approach. In Wu W, Castiglione A, Wen S, editors, Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings. 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

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