Brief Announcement: Deriving Context for Touch Events

Moran Azran; Niv Ben Shabat; Tal Shkolnik; Yossi Oren

doi:10.1007/978-3-319-94147-9_23

Brief Announcement: Deriving Context for Touch Events

Moran Azran, Niv Ben Shabat, Tal Shkolnik, Yossi Oren

Department of Software and Information Systems Engineering

Ben-Gurion University of the Negev

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

Abstract

To quantify the amount of high-level context information which can be derived by observing only a user’s touchscreen interactions, we performed a user study, in which we recorded 160 touch interaction sessions from users running different applications, and then applied both classical machine learning methods and deep learning methods to the results. Our results show that it is possible to derive higher-level user context information based on touch events alone, validating the efficacy of touch injection attacks.

Original language	American English
Title of host publication	Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings
Editors	Itai Dinur, Shlomi Dolev, Sachin Lodha
Publisher	Springer Verlag
Pages	283-286
Number of pages	4
ISBN (Electronic)	978-3-319-94147-9
ISBN (Print)	9783319941462
DOIs	https://doi.org/10.1007/978-3-319-94147-9_23
State	Published - 17 Jun 2018
Event	2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018 - Beer-Sheva, Israel Duration: 21 Jun 2018 → 22 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10879 LNCS

Conference

Conference	2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018
Country/Territory	Israel
City	Beer-Sheva
Period	21/06/18 → 22/06/18

Keywords

Machine learning
Malicious hardware
Smart phone

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-94147-9_23

Cite this

Azran, M., Shabat, N. B., Shkolnik, T., & Oren, Y. (2018). Brief Announcement: Deriving Context for Touch Events. In I. Dinur, S. Dolev, & S. Lodha (Eds.), Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings (pp. 283-286). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10879 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-94147-9_23

Azran, Moran ; Shabat, Niv Ben ; Shkolnik, Tal et al. / Brief Announcement : Deriving Context for Touch Events. Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings. editor / Itai Dinur ; Shlomi Dolev ; Sachin Lodha. Springer Verlag, 2018. pp. 283-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Brief Announcement: Deriving Context for Touch Events",

abstract = "To quantify the amount of high-level context information which can be derived by observing only a user{\textquoteright}s touchscreen interactions, we performed a user study, in which we recorded 160 touch interaction sessions from users running different applications, and then applied both classical machine learning methods and deep learning methods to the results. Our results show that it is possible to derive higher-level user context information based on touch events alone, validating the efficacy of touch injection attacks.",

keywords = "Machine learning, Malicious hardware, Smart phone",

author = "Moran Azran and Shabat, {Niv Ben} and Tal Shkolnik and Yossi Oren",

note = "Funding Information: This research was supported by Israel Science Foundation grants 702/16 and 703/16. Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018 ; Conference date: 21-06-2018 Through 22-06-2018",

year = "2018",

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doi = "10.1007/978-3-319-94147-9_23",

language = "American English",

isbn = "9783319941462",

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

publisher = "Springer Verlag",

pages = "283--286",

editor = "Itai Dinur and Shlomi Dolev and Sachin Lodha",

booktitle = "Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings",

address = "Germany",

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Azran, M, Shabat, NB, Shkolnik, T & Oren, Y 2018, Brief Announcement: Deriving Context for Touch Events. in I Dinur, S Dolev & S Lodha (eds), Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10879 LNCS, Springer Verlag, pp. 283-286, 2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018, Beer-Sheva, Israel, 21/06/18. https://doi.org/10.1007/978-3-319-94147-9_23

Brief Announcement: Deriving Context for Touch Events. / Azran, Moran; Shabat, Niv Ben; Shkolnik, Tal et al.
Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings. ed. / Itai Dinur; Shlomi Dolev; Sachin Lodha. Springer Verlag, 2018. p. 283-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10879 LNCS).

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

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AU - Shabat, Niv Ben

AU - Shkolnik, Tal

AU - Oren, Yossi

PY - 2018/6/17

Y1 - 2018/6/17

N2 - To quantify the amount of high-level context information which can be derived by observing only a user’s touchscreen interactions, we performed a user study, in which we recorded 160 touch interaction sessions from users running different applications, and then applied both classical machine learning methods and deep learning methods to the results. Our results show that it is possible to derive higher-level user context information based on touch events alone, validating the efficacy of touch injection attacks.

AB - To quantify the amount of high-level context information which can be derived by observing only a user’s touchscreen interactions, we performed a user study, in which we recorded 160 touch interaction sessions from users running different applications, and then applied both classical machine learning methods and deep learning methods to the results. Our results show that it is possible to derive higher-level user context information based on touch events alone, validating the efficacy of touch injection attacks.

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KW - Malicious hardware

KW - Smart phone

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DO - 10.1007/978-3-319-94147-9_23

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

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BT - Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings

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Azran M, Shabat NB, Shkolnik T, Oren Y. Brief Announcement: Deriving Context for Touch Events. In Dinur I, Dolev S, Lodha S, editors, Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings. Springer Verlag. 2018. p. 283-286. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-94147-9_23

Brief Announcement: Deriving Context for Touch Events

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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