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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2018: Intelligent Information and Database Systems pp 315–323Cite as

Enhanced Passcode Recognition Based on Press Force and Time Interval

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 10752)

Abstract

Mobile devices applied to many businesses applications. Constructing a useful security system for a mobile device is necessary. As to lots of personal information on the mobile device, it must protect completely. Now, the mobile device has many protection mechanisms. However, even we have these mechanisms that still cannot protect someone who wants to use your passcode to intrude private information. The 3D-Touch features use people’s habit and action. According to these functions, we construct them to become a decision tree. In this paper, we will use 3D-Touch techniques to propose an enhanced passcode mechanisms for protection mobile device system. Experimental results show that the method is feasible and efficient.

Keywords

  • 3D-touch
  • Decision tree
  • Machine learning
  • Identity recognition

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References

  1. Rainie, L.: Tablet and e-book reader ownership nearly double over the holiday gift-giving period. Pew Internet. Pew Research Center (2012)

    Google Scholar 

  2. Smith, A.: Nearly half of American adults are smartphone owners. Pew Internet. Pew Research Center (2012)

    Google Scholar 

  3. Hooten, E.R., Hayes, S.T., Adams, J.A.: Communicative modalities for mobile device interaction. Hum. Comput. Stud. 71, 998–1002 (2013)

    CrossRef  Google Scholar 

  4. http://www.tahrd.ntnu.edu.tw/files/archive/157_9684612a.pdf

  5. Kruger, H., Kearney, W.: A prototype for assessing information security awareness. Comput. Secur. 25, 223–229 (2006)

    CrossRef  Google Scholar 

  6. Stirling, D., Pan, Z., Ros, M.: Recognizing human motions through mixture modeling of inertial data Matthew field. Pattern Recognit. 48, 2394–2406 (2015)

    CrossRef  Google Scholar 

  7. Green, N.: Who’s watching whom? Monitoring and accountability in mobile relations. In: Brown, B., Green, N., Harper, R. (eds.) Wireless World. Computer Supported Cooperative Work, pp. 32–45. Springer, London (2002). https://doi.org/10.1007/978-1-4471-0665-4_3

    CrossRef  Google Scholar 

  8. Jones, O., Williams, M., Fleuriot, C.: “A new sense of place?” The implications of mobile wearable ICT devices for the geographies of urban childhood. Child. Geograph. 1(2), 165–180 (2003)

    CrossRef  Google Scholar 

  9. Fox S.: Online Banking. Report of Pew Internet (2013). http://www.pewinternet.org/Reports/2013/online-banking.aspx

  10. Kennedy, N., Sifiso, N.: On privacy calculus and underlying consumer concerns influencing mobile banking subscriptions. In: Information Security for South Africa (ISSA), pp. 1–9, August 2012

    Google Scholar 

  11. Moço, N.F., Correia, P.L., Soares, L.D.: Smartphone-based palmprint recognition system. In: Telecommunications (ICT), pp. 457–461, May 2014

    Google Scholar 

  12. Johansson, G.: Visual motion perception. Sci. Am. 232(6), 76–88 (1975)

    CrossRef  Google Scholar 

  13. Alex, M., Vasilescu, O.: Human motion signatures: analysis, synthesis, recognition. Department of Computer Science, University of Toronto, vol. 3, pp. 456–460. IEEE, August 2002

    Google Scholar 

  14. Lqbal, M.R.A., Rahman, S., Nabil S.I.: Knowledge based decision tree construction with feature importance domain knowledge. In: Electrical Computer Engineering (ICECE), pp. 659–662, December 2012

    Google Scholar 

  15. Clayton, A.: A Practical Guide to Knowledge Acquisition. Addison-Wesley, Reading (1991)

    MATH  Google Scholar 

  16. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  17. Mitchell T.M.: Artificial Neural Networks, pp. 81–126. McGraw-Hill Science/Engineering/Math (1997)

    Google Scholar 

  18. Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  19. Imam, I.F., Michalski, R.S.: Should decision trees be learned from examples or from decision rules? In: Komorowski, J., Raś, Z.W. (eds.) ISMIS 1993. LNCS, vol. 689, pp. 395–404. Springer, Heidelberg (1993). https://doi.org/10.1007/3-540-56804-2_37

    CrossRef  Google Scholar 

  20. Yukun, C., Yunfeng, L.: An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Syst. Appl. 33, 230–240 (2007)

    CrossRef  Google Scholar 

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Acknowledgements

This work was supported by Ministry of Science and Technology, Taiwan, R.O.C. (Grant No. MOST-104-2221-E-324-019-MY2; MOST-106-2221-E-324; MOST-106-2218-E-324-002).

Author information

Authors and Affiliations

  1. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan

    Hua-Yuan Shih, Rung-Ching Chen & Chen-Yeng Peng

  2. Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan

    Song Guo

Authors
  1. Hua-Yuan Shih
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  2. Song Guo
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  3. Rung-Ching Chen
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  4. Chen-Yeng Peng
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Corresponding author

Correspondence to Rung-Ching Chen .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Quang Binh University, Dong Hoi City, Vietnam

    Duong Hung Hoang

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Rutgers University, Piscataway, New Jersey, USA

    Hoang Pham

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Shih, HY., Guo, S., Chen, RC., Peng, CY. (2018). Enhanced Passcode Recognition Based on Press Force and Time Interval. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_30

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  • DOI: https://doi.org/10.1007/978-3-319-75420-8_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75419-2

  • Online ISBN: 978-3-319-75420-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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