A study of touching behavior for authentication in touch screen smart devices

Ala Abdulhakim Alariki; Azizah  Bt Abdul Manaf; S. Khan

doi:10.1109/INTELSE.2016.7475123

A study of touching behavior for authentication in touch screen smart devices

Ala Abdulhakim Alariki, Azizah Bt Abdul Manaf, S. Khan

Universiti Teknologi Malaysia

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

5 Citations (Scopus)

Abstract

With the increased popularity of touch screen mobile phones, touch gesture behavior is becoming more and more important. Due to increasing demand for safer access in touch screen mobile phones, old strategies like pins, tokens, or passwords have failed to stay abreast of the challenges. However, we study user authentication scheme based on these touch dynamics features for accurate user authentication. We developed the software needed to collect readings from touch screen of mobile phone running the android operation system. Based on these preliminary experiments we concentrated on the Random Forest classifier to differentiate multiple users. Our results show that combining all features such as touch direction, finger pressure, finger size and acceleration correctly classified touch behavior on an android phone with 98.14% accuracy.

Original language	English
Title of host publication	2016 International Conference on Intelligent Systems Engineering (ICISE), 15-17 January 2016, Islamabad, Pakistan
Place of Publication	U.S.
Publisher	IEEE
Pages	216-221
Number of pages	6
ISBN (Electronic)	9781467387521
DOIs	https://doi.org/10.1109/INTELSE.2016.7475123
Publication status	Published - 2016
Externally published	Yes
Event	International Conference on Intelligent Systems Engineering - Islamabad, Pakistan Duration: 15 Jan 2016 → 17 Jan 2016

Conference

Conference	International Conference on Intelligent Systems Engineering
Abbreviated title	ICISE
Country/Territory	Pakistan
City	Islamabad
Period	15/01/16 → 17/01/16

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10.1109/INTELSE.2016.7475123

Cite this

@inproceedings{a749dfc653ea48868d241a584b28da8b,

title = "A study of touching behavior for authentication in touch screen smart devices",

abstract = "With the increased popularity of touch screen mobile phones, touch gesture behavior is becoming more and more important. Due to increasing demand for safer access in touch screen mobile phones, old strategies like pins, tokens, or passwords have failed to stay abreast of the challenges. However, we study user authentication scheme based on these touch dynamics features for accurate user authentication. We developed the software needed to collect readings from touch screen of mobile phone running the android operation system. Based on these preliminary experiments we concentrated on the Random Forest classifier to differentiate multiple users. Our results show that combining all features such as touch direction, finger pressure, finger size and acceleration correctly classified touch behavior on an android phone with 98.14% accuracy.",

author = "Alariki, {Ala Abdulhakim} and {Bt Abdul Manaf}, Azizah and S. Khan",

year = "2016",

doi = "10.1109/INTELSE.2016.7475123",

language = "English",

pages = "216--221",

booktitle = "2016 International Conference on Intelligent Systems Engineering (ICISE), 15-17 January 2016, Islamabad, Pakistan",

publisher = "IEEE",

note = "International Conference on Intelligent Systems Engineering , ICISE ; Conference date: 15-01-2016 Through 17-01-2016",

}

Alariki, AA, Bt Abdul Manaf, A & Khan, S 2016, A study of touching behavior for authentication in touch screen smart devices. in 2016 International Conference on Intelligent Systems Engineering (ICISE), 15-17 January 2016, Islamabad, Pakistan., 7475123, IEEE, U.S., pp. 216-221, International Conference on Intelligent Systems Engineering , Islamabad, Pakistan, 15/01/16. https://doi.org/10.1109/INTELSE.2016.7475123

A study of touching behavior for authentication in touch screen smart devices. / Alariki, Ala Abdulhakim; Bt Abdul Manaf, Azizah ; Khan, S.
2016 International Conference on Intelligent Systems Engineering (ICISE), 15-17 January 2016, Islamabad, Pakistan. U.S.: IEEE, 2016. p. 216-221 7475123.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - A study of touching behavior for authentication in touch screen smart devices

AU - Alariki, Ala Abdulhakim

AU - Bt Abdul Manaf, Azizah

AU - Khan, S.

PY - 2016

Y1 - 2016

N2 - With the increased popularity of touch screen mobile phones, touch gesture behavior is becoming more and more important. Due to increasing demand for safer access in touch screen mobile phones, old strategies like pins, tokens, or passwords have failed to stay abreast of the challenges. However, we study user authentication scheme based on these touch dynamics features for accurate user authentication. We developed the software needed to collect readings from touch screen of mobile phone running the android operation system. Based on these preliminary experiments we concentrated on the Random Forest classifier to differentiate multiple users. Our results show that combining all features such as touch direction, finger pressure, finger size and acceleration correctly classified touch behavior on an android phone with 98.14% accuracy.

AB - With the increased popularity of touch screen mobile phones, touch gesture behavior is becoming more and more important. Due to increasing demand for safer access in touch screen mobile phones, old strategies like pins, tokens, or passwords have failed to stay abreast of the challenges. However, we study user authentication scheme based on these touch dynamics features for accurate user authentication. We developed the software needed to collect readings from touch screen of mobile phone running the android operation system. Based on these preliminary experiments we concentrated on the Random Forest classifier to differentiate multiple users. Our results show that combining all features such as touch direction, finger pressure, finger size and acceleration correctly classified touch behavior on an android phone with 98.14% accuracy.

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DO - 10.1109/INTELSE.2016.7475123

M3 - Conference Paper

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EP - 221

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PB - IEEE

CY - U.S.

T2 - International Conference on Intelligent Systems Engineering

Y2 - 15 January 2016 through 17 January 2016

ER -

A study of touching behavior for authentication in touch screen smart devices

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