A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 6
Oct.  2020

IEEE/CAA Journal of Automatica Sinica

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Dongxiang Chen, Zhijun Ding, Chungang Yan and Mimi Wang, "A Behavioral Authentication Method for Mobile Based on Browsing Behaviors," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1528-1541, Nov. 2020. doi: 10.1109/JAS.2019.1911648
Citation: Dongxiang Chen, Zhijun Ding, Chungang Yan and Mimi Wang, "A Behavioral Authentication Method for Mobile Based on Browsing Behaviors," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1528-1541, Nov. 2020. doi: 10.1109/JAS.2019.1911648

A Behavioral Authentication Method for Mobile Based on Browsing Behaviors

doi: 10.1109/JAS.2019.1911648
Funds:  This work was partially supported by the National Key Research and Development Program of China (2018YFB2100801)
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  • The passwords for unlocking the mobile devices are relatively simple, easier to be stolen, which causes serious potential security problems. An important research direction of identity authentication is to establish user behavior models to authenticate users. In this paper, a mobile terminal APP browsing behavioral authentication system architecture which synthesizes multiple factors is designed. This architecture is suitable for users using the mobile terminal APP in the daily life. The architecture includes data acquisition, data processing, feature extraction, and sub model training. We can use this architecture for continuous authentication when the user uses APP at the mobile terminal.

     

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    Highlights

    • The core findings: We first found that multiple basic models and traditional algorithm description can be used to model various factors of user behavior, and finally integrated into a comprehensive user behavior model. At the same time, we find that the training model samples can not only be stored in static form, and can be contained in the most original data. We can traverse the original data and train the machine learning model by using the feature vectors generated in the iteration.
    • The essence of the research: We analyze the data of various operation behaviors and external environment when users use mobile app. The model of each user can be used to analyze the user behavior data only to ensure the security when the technology of password and biometric authentication are not used. We integrate a variety of user behavior factors, and also consider the characteristics of the external environment when using mobile devices. We synthesize several basic models to realize our method.
    • The distinction of the paper: The existing methods only consider a single behavior factor, and do not consider the external environment, which leads to the limited potential of the method. We have effectively integrated a variety of machine learning models and studied a series of algorithms on the models. We don't rely purely on machine learning models.
    • Quick textual overview: We build a comprehensive model of user behavior, which integrates multiple user behavior factors with external environmental data. Our idea is to use different machine learning models to process heterogeneous multi-source data, and to build algorithms on top of machine learning models. The method in this paper realizes the iterative generation of feature vectors from the most primitive sensor data to realize the training of machine learning models. And real-time user behavior authentication using basic data is realized.

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