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Soft Computing

, Volume 22, Issue 12, pp 3959–3981 | Cite as

A fuzzy decision support system for multifactor authentication

  • Arunava Roy
  • Dipankar Dasgupta
Methodologies and Application
  • 240 Downloads

Abstract

Multifactor authentication (MFA) is a growing trend for the accurate identification of the legitimate users through different modalities such as biometrics, nonbiometric, and cognitive behavior metric. In this paper, we have developed an adaptive MFA that considers the effects of different user devices, media, environments, and the frequency of authentication to detect the legitimate user. For this purpose, initially, we have evaluated the trustworthiness values of all the authentication modalities in different user devices and media using a nonlinear programming problem with probabilistic constraints. Finally, an evolutionary strategy, using fuzzy “IF–THEN” rule and genetic algorithm has been developed for the adaptive selection of authentication modalities. We have done a numerical simulation to prove the effectiveness and efficiency of the proposed method. Moreover, we have developed a prototype client–server-based application and have done a detailed user study to justify its better usability than the existing counterparts.

Keywords

Multifactor authentication (MFA) Authentication modalities Active authentication Fuzzy decision support system Genetic algorithm Optimal selection strategy 

Notes

Acknowledgements

The authors are also thankful to The University of Memphis, TN, USA and the National University of Singapore (NUS) for providing all the necessary supports to continue this research work. The authors are also thankful to the extremely learned reviewers for their valuable suggestions for the improvement of the paper.

Compliance with ethical standards

Conflict of interest

Author Dr. Arunava Roy declares that he has no conflict of interest. Author Prof. Dipankar Dasgupta declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MemphisMemphisUSA
  2. 2.Department of Industrial and Systems EngineeringNational University of SingaporeSingaporeSingapore

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