WheelLogger: Driver Tracing Using Smart Watch

Joon Young Park; Jong Pil Yun; Dong Hoon Lee

doi:10.1007/978-3-319-93563-8_8

WheelLogger: Driver Tracing Using Smart Watch

Joon Young Park, Jong Pil Yun, Dong Hoon Lee

School of Cybersecurity

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Location-related data is one of the most sensitive data for user privacy. Theft of location-related information on mobile device poses serious threats to users. Even though the extant confirmation of permissions feature on modern smart devices can prevent direct leakage of information from location-related sensors, recent research has shown that leakage of location-related information is possible through indirect, side-channel attacks. In this paper, we show that the travel path of a vehicle can be inferred without acknowledging the user using a zero-permission smart watch application. The sensor we used in our experiment is the accelerometer sensor on Apple Watch. We find that a targeted user can be traced with 83% accuracy. We suggest that our approach may be used to successfully attack other smart phone devices because it was successful on Apple Watch, which is considered as the most constrained device in the market. This result shows that the zero-permission application on a smart watch, if manipulated adequately, can transform into a high-threat malware.

Original language	English
Title of host publication	Information Security Applications - 18th International Conference, WISA 2017, Revised Selected Papers
Editors	Brent ByungHoon Kang, Taesoo Kim
Publisher	Springer Verlag
Pages	87-100
Number of pages	14
ISBN (Print)	9783319935621
DOIs	https://doi.org/10.1007/978-3-319-93563-8_8
Publication status	Published - 2018
Event	18th World International Conference on Information Security and Application, WISA 2017 - Jeju Island, Korea, Republic of Duration: 2017 Aug 24 → 2017 Aug 26

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10763 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	18th World International Conference on Information Security and Application, WISA 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	17/8/24 → 17/8/26

Bibliographical note

Funding Information:
Acknowledgement. This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R7117-17-0161, Anomaly detection framework for autonomous vehicles).

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-93563-8_8

Cite this

Park, J. Y., Yun, J. P., & Lee, D. H. (2018). WheelLogger: Driver Tracing Using Smart Watch. In B. B. Kang, & T. Kim (Eds.), Information Security Applications - 18th International Conference, WISA 2017, Revised Selected Papers (pp. 87-100). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10763 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93563-8_8

WheelLogger: Driver Tracing Using Smart Watch. / Park, Joon Young; Yun, Jong Pil; Lee, Dong Hoon.
Information Security Applications - 18th International Conference, WISA 2017, Revised Selected Papers. ed. / Brent ByungHoon Kang; Taesoo Kim. Springer Verlag, 2018. p. 87-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10763 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Park, JY, Yun, JP & Lee, DH 2018, WheelLogger: Driver Tracing Using Smart Watch. in BB Kang & T Kim (eds), Information Security Applications - 18th International Conference, WISA 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10763 LNCS, Springer Verlag, pp. 87-100, 18th World International Conference on Information Security and Application, WISA 2017, Jeju Island, Korea, Republic of, 17/8/24. https://doi.org/10.1007/978-3-319-93563-8_8

Park JY, Yun JP, Lee DH. WheelLogger: Driver Tracing Using Smart Watch. In Kang BB, Kim T, editors, Information Security Applications - 18th International Conference, WISA 2017, Revised Selected Papers. Springer Verlag. 2018. p. 87-100. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93563-8_8

Park, Joon Young ; Yun, Jong Pil ; Lee, Dong Hoon. / WheelLogger : Driver Tracing Using Smart Watch. Information Security Applications - 18th International Conference, WISA 2017, Revised Selected Papers. editor / Brent ByungHoon Kang ; Taesoo Kim. Springer Verlag, 2018. pp. 87-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Location-related data is one of the most sensitive data for user privacy. Theft of location-related information on mobile device poses serious threats to users. Even though the extant confirmation of permissions feature on modern smart devices can prevent direct leakage of information from location-related sensors, recent research has shown that leakage of location-related information is possible through indirect, side-channel attacks. In this paper, we show that the travel path of a vehicle can be inferred without acknowledging the user using a zero-permission smart watch application. The sensor we used in our experiment is the accelerometer sensor on Apple Watch. We find that a targeted user can be traced with 83% accuracy. We suggest that our approach may be used to successfully attack other smart phone devices because it was successful on Apple Watch, which is considered as the most constrained device in the market. This result shows that the zero-permission application on a smart watch, if manipulated adequately, can transform into a high-threat malware.",

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WheelLogger: Driver Tracing Using Smart Watch

Abstract

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ASJC Scopus subject areas

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