TY - GEN
T1 - Unsure how to authenticate on your VR headset? Come on, use your head!
AU - Mustafa, Tahrima
AU - Matovu, Richard
AU - Serwadda, Abdul
AU - Muirhead, Nicholas
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/3/21
Y1 - 2018/3/21
N2 - For security-sensitive Virtual Reality (VR) applications that require the end-user to enter authenticatioan credentials within the virtual space, a VR user’s inability to see (potentially malicious entities in) the physical world can be discomforting, and in the worst case could potentially expose the VR user to visual attacks. In this paper, we show that the head, hand and (or) body movement patterns exhibited by a user freely interacting with a VR application contain user-specific information that can be leveraged for user authentication. For security-sensitive VR applications, we argue that such functionality can be used as an added layer of security that minimizes the need for entering the PIN. Based on a dataset of 23 users who interacted with our VR application for two sessions over a period of one month, we obtained mean equal error rates as low as 7% when we authenticated users based on their head and body movement patterns.
AB - For security-sensitive Virtual Reality (VR) applications that require the end-user to enter authenticatioan credentials within the virtual space, a VR user’s inability to see (potentially malicious entities in) the physical world can be discomforting, and in the worst case could potentially expose the VR user to visual attacks. In this paper, we show that the head, hand and (or) body movement patterns exhibited by a user freely interacting with a VR application contain user-specific information that can be leveraged for user authentication. For security-sensitive VR applications, we argue that such functionality can be used as an added layer of security that minimizes the need for entering the PIN. Based on a dataset of 23 users who interacted with our VR application for two sessions over a period of one month, we obtained mean equal error rates as low as 7% when we authenticated users based on their head and body movement patterns.
KW - Behavioral Biometrics
KW - Continuous Authentication
KW - Head Movement Patterns
KW - Virtual Reality
UR - http://www.scopus.com/inward/record.url?scp=85052027381&partnerID=8YFLogxK
U2 - 10.1145/3180445.3180450
DO - 10.1145/3180445.3180450
M3 - Conference contribution
AN - SCOPUS:85052027381
T3 - IWSPA 2018 - Proceedings of the 4th ACM International Workshop on Security and Privacy Analytics, Co-located with CODASPY 2018
SP - 23
EP - 30
BT - IWSPA 2018 - Proceedings of the 4th ACM International Workshop on Security and Privacy Analytics, Co-located with CODASPY 2018
PB - Association for Computing Machinery, Inc
T2 - 4th ACM International Workshop on Security and Privacy Analytics, IWSPA 2018
Y2 - 21 March 2018
ER -