Skip to main content
Log in

RETRACTED ARTICLE: Continuous user authentication based score level fusion with hybrid optimization

  • Published:
Cluster Computing Aims and scope Submit manuscript

This article was retracted on 06 December 2022

This article has been updated

Abstract

With the fast growing demands in authentication biometric-based authentication system has been widely utilized in many applications which require secured personal identification/verification. In the existing computer systems, there is a possibility of an imposter gaining access when a user session is active and the user moves away from the system. To solve this problem, proposes a continuous user authentication scheme. Continuous authentication (CA) system verifies the user continuously once a person is logged in. CA system prevents the intruders from invoking the system. It passively verifies the system without interrupting the users work progress. In this paper, score level fusion is carried out using optimization that is genetic particle swarm optimization and classifiers lazy classifier—Naïve Bayes held for recognition process. The main objective of the proposed method is to fuse the user biometric traits and to accomplish the optimal result in the continuous user authentication. The proposed technique consists of four modules, namely processing module, feature extraction module, fusion module and recognition module. Finally, the proposed fusion method is applied to remote biometric authentication. The implementation is carried out using MATLAB and the evaluation metrics employed are false acceptance rate, false rejection rate and accuracy, sensitivity and specificity. From the results, we can observe that the proposed technique has achieved better performance metrics values for continuous authentication process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Change history

References

  1. Mezai, L., Hachouf, F.: Score-level fusion of face and voice using particle swarm optimization and belief functions. IEEE Trans. Hum. Mach. Syst. 45(6), 761–772 (2015)

    Article  Google Scholar 

  2. Alsultan, A., Warwick, K., Wei, H.: Non-conventional keystroke dynamics for user authentication. Pattern Recogn. Lett. 89, 53–59 (2017)

    Article  Google Scholar 

  3. Camara, C., Peris-Lopez, P., Gonzalez-Manzano, L., Tapiador, J.: Real-time electrocardiogram streams for continuous authentication. Appl. Soft Comput. 1–22 (2017)

  4. Alpar, O.: Frequency spectrograms for biometric keystroke authentication using neural network-based classifier. Knowl. Based Syst. 116, 163–171 (2017)

    Article  Google Scholar 

  5. Bailey, K.O., Okolica, J.S., Peterson, G.L.: User identification and authentication using multi-modal behavioral biometrics. Comput. Secur. 43, 77–89 (2014)

    Article  Google Scholar 

  6. Pérez-Caballero, G., Andrade, J.M., Olmos, P., Molina, Y., Jiménez, I., Durán, J.J., et al.: Authentication of tequilas using pattern recognition and supervised classification. TrAC Trends Anal. Chem. 1–44 (2017)

  7. Schmandt, J., Sherman, A.T., Banerjee, N.: Mini-MAC: raising the bar for vehicular security with a lightweight message authentication protocol. Veh. Commun. 1–9 (2017)

  8. Polak, A., Kelman, T., Murray, P., Marshall, S., Stothard, D.J., Eastaugh, N., Eastaugh, F.: Hyperspectral imaging combined with data classification techniques as an aid for artwork authentication. J. Cult. Herit. 1–11 (2017)

  9. Peng, J., El-Latif, A.A.A., Li, Q., Niu, X.: Multimodal biometric authentication based on score level fusion of finger biometrics. Opt. Int. J. Light Electron Opt. 125(23), 6891–6897 (2014)

    Article  Google Scholar 

  10. Rodrıguez-Liñares, L., Garcıa-Mateo, C., Alba-Castro, J.L.: On combining classifiers for speaker authentication. Pattern Recogn. 36(2), 347–359 (2003)

    Article  Google Scholar 

  11. Saevanee, H., Clarke, N., Furnell, S., Biscione, V.: Continuous user authentication using multi-modal biometrics. Comput. Secur. 53, 234–246 (2015)

    Article  Google Scholar 

  12. Swamy, G.J., Muthukumarappan, K.: Optimization of continuous and intermittent microwave extraction of pectin from banana peels. Food Chem. 220, 108–114 (2017)

    Article  Google Scholar 

  13. Mondal, S., Bours, P.: A study on continuous authentication using a combination of keystroke and mouse biometrics. Neurocomputing 230, 1–22 (2017)

  14. Zhang, Y., Sun, D., Qiu, Z.: Hand-based single sample biometrics recognition. Neural Comput. Appl. 21(8), 1835–1844 (2012)

    Article  Google Scholar 

  15. Meraoumia, A., Chitroub, S., Bouridane, A.: Fusion of finger-knuckle-print and palm print for an efficient multi-biometric system of person recognition. In: IEEE International Conference on Communications (ICC), pp. 1–5 (2011)

  16. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)

    Article  Google Scholar 

  17. Jiwnani, G., Student, M.: Multi-modal biometric authentication using fingerprint and iris. J. Comput. Sci. Commun. Netw. 5(2), 115–119

  18. Skračić, K., Pale, P., Kostanjčar, Z.: Authentication approach using one-time challenge generation based on user behavior patterns captured in transactional datasets. Comput. Secur. 67, 107–121 (2017)

    Article  Google Scholar 

  19. Fenu, G., Maras, M., Boratto, L.: A multi-biometric system for continuous student authentication in e-learning platforms. Pattern Recognit. Lett. 1–10 (2017)

  20. Grover, J., Hanmandlu, M.: Hybrid fusion of score level and adaptive fuzzy decision level fusions for the finger-knuckle-print based authentication. Appl. Soft Comput. 31, 1–13 (2015)

    Article  Google Scholar 

  21. Sim, H.M., Asmuni, H., Hassan, R., Othman, R.M.: Multimodal biometrics: weighted score level fusion based on non-ideal iris and face images. J. Expert Syst Appl. 41(11), 5390–5404 (2014)

  22. Mitra, S.: Inference for performance evaluation of fingerprint identification systems based on a hierarchical random effects model. Commun. Stat. 1(3), 136–150 (2015)

  23. Bailey, K.O., Okolica, J.S., Peterson, G.L.: User identification and authentication using multi-modal behavioral biometrics. Comput. Secur. 43, 77–89 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Prakash.

Additional information

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03898-4

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prakash, A. RETRACTED ARTICLE: Continuous user authentication based score level fusion with hybrid optimization. Cluster Comput 22 (Suppl 5), 12959–12969 (2019). https://doi.org/10.1007/s10586-018-1819-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1819-6

Keywords

Navigation