Keystroke Rhythm Analysis Based on Dynamics of Fingertips

  • SurajEmail author
  • Parthana Sarma
  • Amit Kumar Yadav
  • Amit Kumar Yadav
  • Shovan Barma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)


The proposed work presents an analysis of rhythmic patterns based on the dynamics of the fingertips observed during keystroke events on a traditional computer keyboard. In this work, a detailed analysis of pressure and acceleration applied by the user has been taken into consideration, in contrast to the earlier works which have focused primarily on the timing variations of the keys. In this purpose, two different types of sensors pressure and accelerometer were embedded on a traditional keyboard. Groupings of numerical digits and special keys were used to design different kinds of tasks to acquire sensor and timing variations of each keystroke event. In total, six subjects (two females and four males) were asked to provide the data to validate the proposed idea. Subsequently, two types of analysis, interpersonal and intrapersonal keystroke rhythm, have been carried out. The analysis results show uniquely identifiable keystroke sensor and timing variations for different users. However, individual users almost maintained his/her keystroke pressure and acceleration variation irrespective of the type of tasks. Such results demonstrate that keystroke rhythm based on pressure and acceleration variations of the fingertips can be used as a behavioural feature for developing more sophisticated biometric systems for intrusion detection.


Keystroke dynamics Behavioural biometrics Fingertip dynamics MEMS sensors 


  1. 1.
    De Marsico, M., Galdi, C., Nappi, M., Riccio, D.: Firme: face and iris recognition for mobile engagement. Image Vis. Comput. 32(12), 1161–1172 (2014)CrossRefGoogle Scholar
  2. 2.
    Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)Google Scholar
  3. 3.
    Labati, R.D., Genovese, A., Piuri, V., Scotti, F.: Toward unconstrained fingerprint recognition: a fully touchless 3-d system based on two views on the move. IEEE Trans. Syst. Man Cybern. Syst. 46(2), 202–219 (2016)CrossRefGoogle Scholar
  4. 4.
    Zheng, N., Bai, K., Huang, H., Wang, H.: You are how you touch: user verification on smartphones via tapping behaviors. In: IEEE 22nd International Conference on Network Protocols (ICNP 2014), pp. 221–232. IEEE, USA (2014)Google Scholar
  5. 5.
    Monrose, F., Rubin, A.D.: Keystroke dynamics as a biometric for authentication. Future Gener. Comput. Syst. 16(4), 351–359 (2000)CrossRefGoogle Scholar
  6. 6.
    Sim, T., Janakiraman, R.: Are digraphs good for free-text keystroke dynamics? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’07), pp. 1–6. IEEE, USA (2007)Google Scholar
  7. 7.
    Forsen, G.E., Nelson, M.R., Staron Jr., R.J.: Personal attributes authentication techniques. Technical report, Pattern Analysis and Recognition Corp. Rome (1977)Google Scholar
  8. 8.
    Peacock, A., Ke, X., Wilkerson, M.: Typing patterns: a key to user identification. IEEE Secur. Priv. 2(5), 40–47 (2004)CrossRefGoogle Scholar
  9. 9.
    Gaines, R.S., Lisowski, W., Press, S.J., Shapiro, N.: Authentication by keystroke timing: some preliminary results. Technical Report, Rand Corp. Santa Monica (1980)Google Scholar
  10. 10.
    Nonaka, H., Kurihara, M.: Sensing pressure for authentication system using keystroke dynamics. Int. J. Comput. Intell. 1(1), 19–22 (2004)Google Scholar
  11. 11.
    Giuffrida, C., Majdanik, K., Conti, M., Bos, H.: I sensed it was you: authenticating mobile users with sensor-enhanced keystroke dynamics. In: International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment. pp. 92–111, Springer, Berlin (2014)Google Scholar
  12. 12.
    Saevanee, H., Bhattarakosol, P.: Authenticating user using keystroke dynamics and finger pressure. In: 2009 6th IEEE Consumer Communications and Networking Conference (CCNC), pp. 1–2. IEEE, USA (2009)Google Scholar
  13. 13.
    Conti, M., Zachia-Zlatea, I., Crispo, B.: Mind how you answer me!: transparently authenticating the user of a smartphone when answering or placing a call. In: Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, pp. 249–259. ACM, USA (2011)Google Scholar
  14. 14.
    Kambourakis, G., Damopoulos, D., Papamartzivanos, D., Pavlidakis, E.: Introducing touchstroke: keystroke-based authentication system for smartphones. Secur. Commun. Netw. 9(6), 542–554 (2016)CrossRefGoogle Scholar
  15. 15.
    Rehim, R.: Effective python penetration testing. Packt Publishing Ltd, Birmingham (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Suraj
    • 1
    Email author
  • Parthana Sarma
    • 1
  • Amit Kumar Yadav
    • 1
  • Amit Kumar Yadav
    • 1
  • Shovan Barma
    • 1
  1. 1.Department of Electronics and Communication EngineeringIndian Institute of Information Technology GuwahatiGuwahatiIndia

Personalised recommendations