TY - GEN
T1 - Poster
T2 - 17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019
AU - Alawami, Mohsen A.
AU - Aiken, William
AU - Kim, Hyoungshick
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/6/12
Y1 - 2019/6/12
N2 - Existing continuous authentication proposals tend to have two major drawbacks. First, touch-based smartphone authentication approaches typically require explicit user interactions with the smartphone to collect sufficient touch data. These approaches may provide an attacker the opportunity to steal a victim's sensitive data before the system detects the attacker's intrusion. Likewise, an attacker may disable the continuous authentication scheme itself before detection. Second, sensor-based continuous authentication approaches inherently suffer from high energy consumption due to the constant usage of multiple sensors. In this paper, we present a novel continuous authentication system that collects light sensor data from a user's smartphone and analyzes them to authenticate users using support vector machines. We focus on the possibility of collecting light sensor data from users' smartphones while they are conducting daily behaviors to develop an anomaly detection system.
AB - Existing continuous authentication proposals tend to have two major drawbacks. First, touch-based smartphone authentication approaches typically require explicit user interactions with the smartphone to collect sufficient touch data. These approaches may provide an attacker the opportunity to steal a victim's sensitive data before the system detects the attacker's intrusion. Likewise, an attacker may disable the continuous authentication scheme itself before detection. Second, sensor-based continuous authentication approaches inherently suffer from high energy consumption due to the constant usage of multiple sensors. In this paper, we present a novel continuous authentication system that collects light sensor data from a user's smartphone and analyzes them to authenticate users using support vector machines. We focus on the possibility of collecting light sensor data from users' smartphones while they are conducting daily behaviors to develop an anomaly detection system.
UR - https://www.scopus.com/pages/publications/85069186366
U2 - 10.1145/3307334.3328625
DO - 10.1145/3307334.3328625
M3 - Conference contribution
AN - SCOPUS:85069186366
T3 - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
SP - 560
EP - 561
BT - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
PB - Association for Computing Machinery, Inc
Y2 - 17 June 2019 through 21 June 2019
ER -