TY - JOUR
T1 - Behavioral Biometrics for Continuous Authentication in the Internet-of-Things Era
T2 - An Artificial Intelligence Perspective
AU - Liang, Yunji
AU - Samtani, Sagar
AU - Guo, Bin
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - In the Internet-of-Things (IoT) era, user authentication is essential to ensure the security of connected devices and the customization of passive services. However, conventional knowledge-based and physiological biometric-based authentication systems (e.g., password, face recognition, and fingerprints) are susceptible to shoulder surfing attacks, smudge attacks, and heat attacks. The powerful sensing capabilities of IoT devices, including smartphones, wearables, robots, and autonomous vehicles enable continuous authentication (CA) based on behavioral biometrics. The artificial intelligence (AI) approaches hold significant promise in sifting through large volumes of heterogeneous biometrics data to offer unprecedented user authentication and user identification capabilities. In this survey article, we outline the nature of CA in IoT applications, highlight the key behavioral signals, and summarize the extant solutions from an AI perspective. Based on our systematic and comprehensive analysis, we discuss the challenges and promising future directions to guide the next generation of AI-based CA research.
AB - In the Internet-of-Things (IoT) era, user authentication is essential to ensure the security of connected devices and the customization of passive services. However, conventional knowledge-based and physiological biometric-based authentication systems (e.g., password, face recognition, and fingerprints) are susceptible to shoulder surfing attacks, smudge attacks, and heat attacks. The powerful sensing capabilities of IoT devices, including smartphones, wearables, robots, and autonomous vehicles enable continuous authentication (CA) based on behavioral biometrics. The artificial intelligence (AI) approaches hold significant promise in sifting through large volumes of heterogeneous biometrics data to offer unprecedented user authentication and user identification capabilities. In this survey article, we outline the nature of CA in IoT applications, highlight the key behavioral signals, and summarize the extant solutions from an AI perspective. Based on our systematic and comprehensive analysis, we discuss the challenges and promising future directions to guide the next generation of AI-based CA research.
KW - Artificial intelligence (AI)
KW - Internet of Things (IoT)
KW - behavioral biometric
KW - body area networks
KW - constrained devices
KW - continuous authentication (CA)
KW - cyber-physical systems data mining
UR - http://www.scopus.com/inward/record.url?scp=85092196283&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2020.3004077
DO - 10.1109/JIOT.2020.3004077
M3 - 文章
AN - SCOPUS:85092196283
SN - 2327-4662
VL - 7
SP - 9128
EP - 9143
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 9
M1 - 9121981
ER -