HMOG: New Behavioral Biometric Features for Continuous
Authentication of Smartphone Users

J 2016

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

SITOVÁ, Zdeňka, Jaroslav ŠEDĚNKA, Qing YANG, Ge PENG, Gang ZHOU et. al.

Basic information

Original name

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

Authors

SITOVÁ, Zdeňka (203 Czech Republic, belonging to the institution), Jaroslav ŠEDĚNKA (203 Czech Republic, belonging to the institution), Qing YANG (156 China), Ge PENG (156 China), Gang ZHOU (156 China), Paolo GASTI (380 Italy) and Kiran BALAGANI (356 India)

Edition

IEEE Transactions on Information Forensics and Security, 2016, 1556-6013

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 4.332

RIV identification code

RIV/00216224:14310/16:00089158

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1109/TIFS.2015.2506542

UT WoS

000372355200001

Keywords in English

Behavioral biometrics; HMOG; biometric key generation; continuous authentication; energy evaluation

Abstract

V originále

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).

Citovat

SITOVÁ, Zdeňka, Jaroslav ŠEDĚNKA, Qing YANG, Ge PENG, Gang ZHOU, Paolo GASTI and Kiran BALAGANI. HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users. IEEE Transactions on Information Forensics and Security. 2016, vol. 11, No 5, p. 877 - 892. ISSN 1556-6013. Available from: https://dx.doi.org/10.1109/TIFS.2015.2506542.

@article{1316101,
   author = {Sitová, Zdeňka and Šeděnka, Jaroslav and Yang, Qing and Peng, Ge and Zhou, Gang and Gasti, Paolo and Balagani, Kiran and Peng, Ge},
   article_number = {5},
   doi = {http://dx.doi.org/10.1109/TIFS.2015.2506542},
   keywords = {Behavioral biometrics; HMOG; biometric key generation; continuous authentication; energy evaluation},
   language = {eng},
   issn = {1556-6013},
   journal = {IEEE Transactions on Information Forensics and Security},
   title = {HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users},
   url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7349202},
   volume = {11},
   year = {2016}
}

TY  - JOUR
ID  - 1316101
AU  - Sitová, Zdeňka - Šeděnka, Jaroslav - Yang, Qing - Peng, Ge - Zhou, Gang - Gasti, Paolo - Balagani, Kiran - Peng, Ge
PY  - 2016
TI  - HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users
JF  - IEEE Transactions on Information Forensics and Security
VL  - 11
IS  - 5
SP  - 877 - 892
EP  - 877 - 892
SN  - 15566013
KW  - Behavioral biometrics
KW  - HMOG
KW  - biometric key generation
KW  - continuous authentication
KW  - energy evaluation
UR  - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7349202
L2  - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7349202
N2  - We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).
ER  -

SITOVÁ, Zdeňka, Jaroslav ŠEDĚNKA, Qing YANG, Ge PENG, Gang ZHOU, Paolo GASTI and Kiran BALAGANI. HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users. \textit{IEEE Transactions on Information Forensics and Security}. 2016, vol.~11, No~5, p.~877 - 892. ISSN~1556-6013. Available from: https://dx.doi.org/10.1109/TIFS.2015.2506542.

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