@inproceedings{3f573b751daf4b7e8fed5e509f8ff3e1,
title = "Multi-source interactive behavior analysis for continuous user authentication on smartphones",
abstract = "Analyzing smartphone users{\textquoteright} behavioral characteristics for recognizing the identities has received growing interest from security and biometric researchers. Extant smartphone authentication methods usually provide one-time identity verification in some specific applications, but the authenticated user is still subject to masquerader attacks or session hijacking. This paper presents a novel smartphone authentication approach by analyzing multi-source user-machine usage behavior (i.e., power consumption, physical sensors, and touchscreen interactions), which can continuously verify the presence of a smartphone user. Extensive experiments are conducted to show that our authentication approach can be up to a relatively high accuracy with an equal-error rate of 5.5\%. This approach can also be seamlessly integrated with existing authentication methods, which does not need additional hardware and is transparent to users.",
keywords = "Continuous authentication, Motion sensor, Smartphone security",
author = "Xiaozi Liu and Chao Shen and Yufei Chen",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 13th Chinese Conference on Biometric Recognition, CCBR 2018 ; Conference date: 11-08-2018 Through 12-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97909-0\_71",
language = "英语",
isbn = "9783319979083",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "669--677",
editor = "Zhenan Sun and Shiguang Shan and Zhenhong Jia and Kurban Ubul and Jie Zhou and Jianjiang Feng and Zhenhua Guo and Yunhong Wang",
booktitle = "Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings",
}