Edge Computing and IoT: Systems, Management and Security. First EAI International Conference, ICECI 2020, Virtual Event, November 6, 2020, Proceedings

Research Article

: Accurate and Transparent User Re-authentication via Finger Touching

  • @INPROCEEDINGS{10.1007/978-3-030-73429-9_7,
        author={Chong Zhang and Songfan Li and Yihang Song and Li Lu and Mengshu Hou},
        title={: Accurate and Transparent User Re-authentication via Finger Touching},
        proceedings={Edge Computing and IoT: Systems, Management and Security. First EAI International Conference, ICECI 2020, Virtual Event, November 6, 2020, Proceedings},
        proceedings_a={ICECI},
        year={2021},
        month={7},
        keywords={User re-authentication User-transparent Touching behavior Biometric capacitance Continuous security},
        doi={10.1007/978-3-030-73429-9_7}
    }
    
  • Chong Zhang
    Songfan Li
    Yihang Song
    Li Lu
    Mengshu Hou
    Year: 2021
    : Accurate and Transparent User Re-authentication via Finger Touching
    ICECI
    Springer
    DOI: 10.1007/978-3-030-73429-9_7
Chong Zhang1, Songfan Li1, Yihang Song1, Li Lu1, Mengshu Hou1
  • 1: University of Electronic Science and Technology of China

Abstract

Re-authentication identifies the user during the whole usage to enhance the security of smartphones. To avoid frequent interrupts to users, user features should be imperceptibly collected for identification without user assistance. Conventionally, behavior habits (.. movement, trail) during the user operation are commonly considered as the most appropriate features for re-authentication. The behavior features, however, are often fluctuating and inevitably sacrifice the accuracy of re-authentication, which puts the phones at risk increasingly. In this paper, we propose , an accurate and transparent scheme for user re-authentication. The basic idea is to leverage the combined information of human biometric capacitance and touching behavior for user identification. When the user touches capacitive-based sensors, both information can be automatically collected and applied in the authentication, which is transparent to the user. Based on the authentication results, we build up user-legitimate models to comprehensively evaluate the user’s legitimacy, which reduces misjudgment and further improves accuracy. Moreover, we implement on an SX9310 EVKA board and conduct comprehensive experiments to evaluate it. The results illustrate that can identify 98% intruders within 10 s, but for legitimate users, the misjudgment is less than 0.9% in 2.6-hours-usage.