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

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(6): 1528-1541
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
shu

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)
More Information
    Abstract
  • 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.

     

    • FullText(HTML)
  • loading
    • References(46)
  • [1]
    iiMedia. 2017−2018 Chinese mobile electricity supplier industry research report. [Online]. Available: http://www.iimedia.cn/61300.html. Accessed May 08, 2018.
    [2]
    iiMedia. 2017−2018 Chinese third party mobile payment market research report. [Online]. Available: http://www.iimedia.cn/61209.html. Accessed Apr. 23, 2018.
    [3]
    S. Minaee and Y. Wang, “Fingerprint recognition using translation invariant scattering network,” arXiv: 1509.03542, 2015.
    [4]
    Y. H. Ding, A. Rattani, and A. Ross, “Bayesian belief models for integrating match scores with liveness and quality measures in a fingerprint verification system,” in Proc. Int. Conf. Biometrics, Halmstad, Sweden, 2016, pp. 1−8.
    [5]
    Chandana, S. Yadav, and M. Mathuria, “Fingerprint recognition based on minutiae information,” Int. J. Comput. Appl., vol. 120, no. 10, pp. 39–42, Jun. 2015.
    [6]
    M. Lastra, J. Carabaño, P. D. Gutiérrez, J. M. Benítez, and F. Herrera, “Fast fingerprint identification using GPUs,” Inf. Sci., vol. 301, pp. 195–214, Apr. 2015. doi: 10.1016/j.ins.2014.12.052
    [7]
    R. D. Labati, A. Genovese, V. Piuri, and F. Scotti, “Toward unconstrained fingerprint recognition: A fully touchless 3-D system based on two views on the move,” IEEE Trans.,Syst.,Man,Cybern., vol. 46, no. 2, pp. 202–219, Feb. 2016. doi: 10.1109/TSMC.2015.2423252
    [8]
    O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” in Proc. British Machine Vision Conf., Swansea, UK, 2015, pp. 41.1−41.12.
    [9]
    Y. Sun, X. G. Wang, and X. O. Tang, “Hybrid deep learning for face verification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 10, pp. 1997–2009, Oct. 2016. doi: 10.1109/TPAMI.2015.2505293
    [10]
    C. X. Ding, J. Choi, D. C. Tao, and L. S. Davis, “Multi-directional multi-level dual-cross patterns for robust face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 3, pp. 518–531, Mar. 2016. doi: 10.1109/TPAMI.2015.2462338
    [11]
    L. L. Liu, T. D. Tran, and S. P. Chin, “Partial face recognition: A sparse representation-based approach,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Shanghai, China, 2016, pp. 2389−2393.
    [12]
    J. Daugman, “New methods in iris recognition,” IEEE Trans. Syst.,Man,Cybern.,Part B (Cybern.), vol. 37, no. 5, pp. 1167–1175, Oct. 2007. doi: 10.1109/TSMCB.2007.903540
    [13]
    Q. Liu, M. M. Wang, P. H. Zhao, C. G. Yan, and Z. J. Ding, “A behavioral authentication method for mobile gesture against resilient user posture,” in Proc. 3rd Int. Conf. Systems and Informatics, Shanghai, China, 2017, pp. 324−331.
    [14]
    A. De Luca, A. Hang, F. Brudy, C. Lindner, and H. Hussmann, “Touch me once and i know it’s you!: Implicit authentication based on touch screen patterns,” in Proc. SIGCHI Conf. Human Factors in Computing Systems, Austin, USA, 2012, pp. 987−996.
    [15]
    Y. H. Wu, “A mobile authentication method based on touchscreen behavior for password pattern,” J. Comput. Inf. Syst., vol. 11, no. 22, pp. 8111–8122, 2015.
    [16]
    T. Feng, Z. Y. Liu, K. A. Kwon, W. D. Shi, B. Carbunar, Y. F. Jiang, and N. Nguyen, “Continuous mobile authentication using touchscreen gestures,” in Proc. IEEE Conf. Technologies for Homeland Security, Waltham, USA, 2013, pp. 451−456.
    [17]
    T. Feng, X. Zhao, B. Carbunar, and W. D. Shi, “Continuous mobile authentication using virtual key typing biometrics,” in Proc. 12th IEEE Int. Conf. Trust, Security and Privacy in Computing and Communications, Melbourne, Australia, 2013, pp. 1547−1552.
    [18]
    M. Frank, R. Biedert, E. Ma, I. Martinovic, and D. Song, “Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication,” IEEE Trans. Inf. Forens. Secur., vol. 8, no. 1, pp. 136–148, Jan. 2013. doi: 10.1109/TIFS.2012.2225048
    [19]
    J. H. Friedman, J. L. Bentley, and R. A. Finkel, “An algorithm for finding best matches in logarithmic expected time,” ACM Trans. Math. Softw., vol. 3, no. 3, pp. 209–226, Sept. 1977. doi: 10.1145/355744.355745
    [20]
    C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, Sept. 1995.
    [21]
    C. Shen, Y. Zhang, Z. M. Cai, T. W. Yu, and X. H. Guan, “Touch-interaction behavior for continuous user authentication on smartphones,” in Proc. Int. Conf. Biometrics, Phuket, Thailand, 2015, pp. 157−162.
    [22]
    B. Schölkopf, R. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt, “Support vector method for novelty detection,” in Proc. 12th Int. Conf. Neural Information Processing Systems, Denver, USA, 1999, pp. 582−588.
    [23]
    S. M. Sagave and B. A. Chaugule, “Continuous touchscreen mobile authentication using several gestures,” Int. J. Emerg. Res. Manage. Technol., vol. 3, no. 6, pp. 52–55, 2014.
    [24]
    T. Feng, J. Yang, Z. X. Yan, E. M. Tapia, and W. D. Shi, “Tips: Context-aware implicit user identification using touch screen in uncontrolled environments,” in Proc. 15th Workshop on Mobile Computing Systems and Applications, Santa Barbara, USA, 2014, pp. 1−6.
    [25]
    H. Xu, Y. F. Zhou, and M. R. Lyu, “Towards continuous and passive authentication via touch biometrics: An experimental study on smartphones,” in Proc. 10th Symp. Usable Privacy and Security, Menlo Park, USA, 2014, pp. 187−198.
    [26]
    A. Roy, T. Halevi, and N. Memon, “An HMM-based multi-sensor approach for continuous mobile authentication,” in Proc. IEEE Military Communications Conf., Tampa, USA, 2015, pp. 1311−1316.
    [27]
    M Ester, H. P. Kriegel, J. Sander, and X. W. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining, Portland, USA, 1996, pp. 226−231.
    [28]
    P. Trikha and S. Vijendra, “Fast density based clustering algorithm,” Int. J. Mach. Learn. Comput., vol. 3, no. 1, pp. 10–12, Feb. 2013.
    [29]
    L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, USA: John Wiley & Sons, 1990.
    [30]
    R. Liu and H. Zhang, “Segmentation of 3D meshes through spectral clustering,” in Proc. 12th Pacific Conf. Computer Graphics and Applications, Seoul, South Korea, 2004, pp. 298−305.
    [31]
    R. T Ng and J. W. Han, “Efficient and effective clustering methods for spatial data mining,” in Proc. 20th Int. Conf. Very Large Data Bases, Santiago de Chile, Chile, 1994, pp. 144−155.
    [32]
    J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, Berkeley, USA, 1967, pp. 281−297.
    [33]
    Z. Fang and C. Zhang, “An improved k-means clustering algorithm,” J. Dalian Nationalities University, vol. 9, no. 1, pp. 44–46, 2011.
    [34]
    P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, Nov. 1987. doi: 10.1016/0377-0427(87)90125-7
    [35]
    R. A Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Human Genet., vol. 7, no. 2, pp. 179–188, Sep. 1936.
    [36]
    A. B. Musa, “Comparative study on classification performance between support vector machine and logistic regression,” Int. J. Mach. Learn. Cybern., vol. 4, no. 1, pp. 13–24, Feb. 2013. doi: 10.1007/s13042-012-0068-x
    [37]
    B. S. Everitt, “Classification and regression trees,” Encyclopedia of Statistics in Behavioral Science, B. S. Everitt and D, Howell, Eds. New York, USA: John Wiley and Sons, Ltd, 2005.
    [38]
    J. R Quinlan, “Induction on decision tree,” Mach. Learn., vol. 1, no. 1, pp. 81–106, 1986.
    [39]
    Y. F. Yao and L. T. Xing, “Improvement of c4.5 decision tree continuous attributes segmentation threshold algorithm and its application,” J. Central South Univ. (Sci. Technol.), vol. 42, no. 12, pp. 3772–3776, Dec. 2011.
    [40]
    C. Y. Zhang and J. Wang, “Attribute weighted naive bayesian classification algorithm,” in Proc. 5th Int. Conf. Computer Science & Education, Hefei, China, 2010, pp. 27−30.
    [41]
    F. J. Pineda, “Generalization of back-propagation to recurrent neural networks,” Phys. Rev. Lett., vol. 59, no. 19, pp. 2229–2232, Nov. 1987. doi: 10.1103/PhysRevLett.59.2229
    [42]
    S. F. Ding, C. Y. Su, and J. Z. Yu, “An optimizing BP neural network algorithm based on genetic algorithm,” Artif. Intell. Rev., vol. 36, no. 2, pp. 153–162, Feb. 2011. doi: 10.1007/s10462-011-9208-z
    [43]
    L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001. doi: 10.1023/A:1010933404324
    [44]
    Y. Freund and R. E. Schapire, “A desicion-theoretic generalization of on-line learning and an application to boosting,” in Proc. 2nd European Conf. Computational Learning Theory, Barcelona, Spain, 1995, pp. 23−37.
    [45]
    L. Breiman, “Bagging predictors,” Mach. Learn., vol. 24, no. 2, pp. 123–140, Aug. 1996.
    [46]
    K. A. Spackman, “Signal detection theory: Valuable tools for evaluating inductive learning,” in Proc. 6th Int. Workshop on Machine Learning, New York, USA, 1989, pp. 160−163.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(10)  / Tables(7)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1674) PDF downloads(78) Cited by()

    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.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return