Securing Smart Offices Through an Intelligent and Multi-device Continuous Authentication System

  • Pedro Miguel Sánchez SánchezEmail author
  • Alberto Huertas Celdrán
  • Lorenzo Fernández Maimó
  • Gregorio Martínez Pérez
  • Guojun Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)


Smart Offices promise the improvement of working conditions in terms of efficiency, productivity and facility. However, new cybersecurity challenges arise associated with the new capabilities of Smart Cities. One of the key challenges is the utilisation of continuous and non-invasive authentication mechanisms since traditional authentication methods have important limitations. Thus, to cover these limitations, the main contribution of this paper is the design and deployment of a continuous and intelligent authentication architecture oriented to Smart Offices. The architecture is oriented to the cloud computing paradigm and considers Machine Learning techniques to authenticate users according to their behaviours. Some experiments demonstrated the suitability of the proposed solution when recognising and authenticating different users using a classification algorithm.


Smart office IoT devices Continuous authentication Behaviour patterns Machine learning Classification 



This work has been partially supported by the Irish Research Council, under the government of Ireland post-doc fellowship (grant code GOIPD/2018/466). Special thanks to all those voluntaries who installed the client applications: Oscar Fernández, Pedro A. Sánchez, Francisco J. Sánchez, Pantaleone Nespoli, Mattia Zago, Sergio López, Manuel Gil, José M. Jorquera and Gregorio Martínez.


  1. 1.
    Suzuki, L.R.: Smart cities IoT: enablers and technology road map. In: Rassia, S.T., Pardalos, P.M. (eds.) Smart City Networks. SOIA, vol. 125, pp. 167–190. Springer, Cham (2017). Scholar
  2. 2.
    Almalki, S., Chatterjee, P., Roy, K.: Continuous authentication using mouse clickstream data analysis. In: Wang, G., Feng, J., Bhuiyan, M.Z.A., Lu, R. (eds.) SpaCCS 2019. LNCS, vol. 11637, pp. 76–85. Springer, Cham (2019). Scholar
  3. 3.
    Fridman, L., et al.: Multi-modal decision fusion for continuous authentication. Comput. Electr. Eng. 41, 142–156 (2015)CrossRefGoogle Scholar
  4. 4.
    Montgomery, M., Chatterjee, P., Jenkins, J., Roy, K.: Touch analysis: an empirical evaluation of machine learning classification algorithms on touch data. In: Wang, G., Feng, J., Bhuiyan, M.Z.A., Lu, R. (eds.) SpaCCS 2019. LNCS, vol. 11611, pp. 147–156. Springer, Cham (2019). Scholar
  5. 5.
    Jorquera Valero, J.M., et al.: Improving the security and QoE in mobile devices through an intelligent and adaptive continuous authentication system. Sensors 18, 3769 (2018)CrossRefGoogle Scholar
  6. 6.
    Bo, C., Zhang, L., Li, X.: SilentSense: Silent User Identification via Dynamics of Touch and Movement Behavioral Biometrics. CoRR, pp. 187–190 (2013)Google Scholar
  7. 7.
    Patel, V.M., Chellappa, R., Chandra, D., Barbello, B.: Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges. IEEE Signal Process. Mag. 33, 49–61 (2016)CrossRefGoogle Scholar
  8. 8.
    Ehatisham-ul Haq, M., et al.: Authentication of smartphone users based on activity recognition and mobile sensing. Sensors 17, 2043 (2017)CrossRefGoogle Scholar
  9. 9.
    Deutschmann, I., Lindholm, J.: Behavioral biometrics for DARPA’s active authentication program. In: International Conference of the BIOSIG Special Interest Group (BIOSIG). Darmstadt, vol. 2013, pp. 1–8 (2013)Google Scholar
  10. 10.
    Aljohani, O., Aljohani, N., Bours, P., Alsolami, F.: Continuous authentication on PCs using artificial immune system. In: 2018 1st International Conference on Computer Applications & Information Security (ICCAIS).
  11. 11.
    Dasgupta, D.: An Overview of Artificial Immune Systems and Their Applications. Springer, Heidelberg (1999). Scholar
  12. 12.
    Mondal, S., Bours, P.: A study on continuous authentication using a combination of keystroke and mouse biometrics. Neurocomputing 230, 1–22 (2017). ISSN 0925–2312CrossRefGoogle Scholar
  13. 13.
    Ashibani, Y., Kauling, D., Mahmoud, Q.H.: Design and implementation of a contextual-based continuous authentication framework for smart homes. Appl. Syst. Innov. 2, 4 (2019)CrossRefGoogle Scholar
  14. 14.
    Nespoli, P., et al.: PALOT: profiling and authenticating users leveraging internet of things. Sensors 19, 2832 (2019)CrossRefGoogle Scholar
  15. 15.
    Operating System Market Share. Stat Counter. Accessed 23 June 2019
  16. 16.
    Python to exe, Python Library. Accessed 22 June 2019
  17. 17.
    pynput, Python Library. Accessed 22 June 2019
  18. 18.
    psutil, Python Library. Accessed 22 June 2019
  19. 19.
    pywin32, Python Library. Accessed 22 June 2019
  20. 20.
    Android Developers, Android Library. Accessed 26 June 2019
  21. 21.
    Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning - ICML 2006.
  22. 22.
    Scikit-learn: Machine Learning in Python, Python Library. Accessed 19 June 2019
  23. 23.
    Python Data Analysis Library, Python Library. Accessed 23 June 2019

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information and Communications EngineeringUniversity of MurciaMurciaSpain
  2. 2.Telecommunication Software and Systems GroupWaterford Institute of TechnologyWaterfordIreland
  3. 3.Department of Computer EngineeringUniversity of MurciaMurciaSpain
  4. 4.School of Computer ScienceGuangzhou UniversityGuangzhouChina

Personalised recommendations