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A fuzzy decision support system for multifactor authentication

  • Methodologies and Application
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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.

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Notes

  1. Brennan et al. (2012), Dasgupta (1999), Dasgupta et al. (2016), Feng and Jain (2011), Guidorizzi (2003), Guntti and Picardi (2005), Jain et al. (2010, 1997), Kang et al. (2014), Locklear et al. (2014), Primo et al. (2014), Parziale and Chen (2009) and Serwadda et al. (2013).

  2. Chatterjee and Roy (2014a, b), Roy (2015), Gomez et al. (2003a, b, c), González et al. (2003), Mamdani (1977), Ross (2010), Zimmermann (1996) and Tanaka (1996).

  3. http://www.mathworks.com/products/matlab/.

  4. http://www.dell.com/downloads/global/products/pedge/en/server-poweredge-r710-tech-guidebook.pdf.

  5. JSON: Java Script Object Notation.

  6. http://www.eembc.org/benchmark/automotive_sl.php.

  7. https://fidoalliance.org/.

  8. https://azure.microsoft.com/en-us/services/multi-factor-authentication/.

References

  • Abramson M, Aha DW (2013) User authentication from web browsing behavior. FLAIRS conference

  • Active Authentication, DARPA (2016) http://www.darpa.mil/program/active-authentication

  • Active Authentication, DARPA (2013) https://www.rsaconference.com/writable/presentations/file_upload/sec-t05_final.pdf

  • Brennan M, Afroz S, Greenstadt R (2012) Adversarial stylometry: circumventing authorship recognition to preserve privacy and anonymity. ACM Trans Inf Syst Secur (TISSEC) 15(3):12–22

    Article  Google Scholar 

  • Chatterjee S, Roy A (2014) Novel algorithms for web software fault prediction. Qual Reliab Eng Int (QREI). doi:10.1002/qre.1687

  • Chatterjee S, Roy A (2014b) Web software fault prediction under fuzzy environment using MODULO-M multivariate overlapping fuzzy clustering algorithm and newly proposed revised prediction algorithm. Appl Soft Comput 22:372–396

    Article  Google Scholar 

  • Chatterjee S, Singh JB, Roy A (2015) A structure-based software reliability allocation using fuzzy analytic hierarchy process. Int J Syst Sci 46(3):513–525

    Article  MathSciNet  MATH  Google Scholar 

  • Dasgupta D (1999) Artificial immune systems and their applications. Springer, Berlin

    Book  MATH  Google Scholar 

  • Dasgupta D, McGregor DR (1993) sGA: A structured genetic algorithm. Department of Computer Science, University of Strathclyde, Glasgow

    Google Scholar 

  • Dasgupta D, Roy A, Nag A (2016) Toward the design of adaptive selection strategies for multi-factor authentication. Comput Secur. doi:10.1016/j.cose.2016.09.0004

  • Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York

    Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

  • Deutschmann I, Lindholm J (2013) Behavioral biometrics for DARPA’s active authentication program. International conference of the biometrics special interest group (BIOSIG). IEEE, pp 1–8

  • Duc NM, Minh BQ (2009) Your face is NOT your password Face Authentication By—Passing Lenovo–Asus–Toshiba. Black Hat Briefings

  • Feng J, Jain AK (2011) Fingerprint reconstruction: from minutiae to phase. IEEE Trans Pattern Anal Mach Intell 33(2):209–223

    Article  Google Scholar 

  • Gomez J, Dasgupta D, Gonzalez F (2003a) Detecting cyber attackswith fuzzy data mining techniques. In: Proceedings of the third SIAMinternational conference on data mining, pp 1–4

  • Gomez J, Dasgupta D, Nasraoui O, Gonzalez F (2003b) Complete expression trees for evolving fuzzy classifier systems with genetic algorithms and application to network intrusion detection . In: Proceedings of the North American fuzzy information processing society (NAFIPS), pp 469–474

  • Gomez J, Gonzalez F, Dasgupta D (2003c) An immune-fuzzy approach to anomaly detection. In: Proceedings of the twelfth IEEE international conference on fuzzy systems (FUZZIEEE), pp 1219–1224

  • González F, Gómez J, Kaniganti M, Dasgupta D (2003) An evolutionary approach to generate fuzzy anomaly signatures. In: Proceedings of the fourth annual ieee information assurance workshop. West Point, New York, pp 251–259

  • Guidorizzi RP (2003) Security: active authentication. IT Prof 15:4–7

    Article  Google Scholar 

  • Guntti D, Picardi C (2005) Keystroke analysis of free text. ACM Trans Inf Syst Secur 8:312–347

    Article  Google Scholar 

  • Hwang S, Lee H, Cho S (2006) Improving authentication accuracy of unfamiliar passwords with pauses and cues for keystroke dynamics-based authentication. In: Chen H, Wang FY, Yang CC, Zeng D, Chau M, Chang K (eds) Intelligence and security informatics. Lecture Notes in Computer Science, vol 3917. Springer, Berlin, Heidelberg, pp 73–78

  • Jain AK, Feng J, Nandakumar K (2010) Fingerprint matching. Computer 43:36–44

    Article  Google Scholar 

  • Jain AK, Hong L, Pankanti S, Bolle R (1997) An identity authentication system using fingerprints. Proc. IEEE 85(9):1365–1388

    Article  Google Scholar 

  • Kang H, Slezak D (2010) Security technology. Disaster recovery and business continuity. Springer, Berlin

    Google Scholar 

  • Kang J, Nyang D, Lee K (2014) Two-factor face authentication using matrix permutation transformation and a user password. Inf Sci 269:1–20

    Article  MathSciNet  Google Scholar 

  • Kaufmann A (1975) Introduction to the theory of fuzzy subsets. Academic Press, London

    MATH  Google Scholar 

  • Lin IC, Chang CC (2009) A countable and time-bound password-based user authentication scheme for the applications of electronic commerce. Inf Sci 179:1269–1277

    Article  MathSciNet  MATH  Google Scholar 

  • Locklear H, Sitova Z, Govindarajan S, Goodkind A, Brizan DG, Gasti P (2014) Continuous authentication with cognition-centric text production and revision features. Presented at the international joint conference on biometrics (IJCB), Clearwater

  • Lucas B, Kanade T (1981) An integrative image registration technique with an application in stereo vision. In: Proceedings of the 7th international joint conference on artificial intelligence, pp 674–679

  • Luenberger DG, Ye Y (2008) Linear and nonlinear programming. Springer, Stanford

    MATH  Google Scholar 

  • Mamdani EH (1977) Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans Comput C–26:1182–1191

    Article  MATH  Google Scholar 

  • Melanie M (1999) An introduction to genetic algorithms. MIT Press, Cambridge

    MATH  Google Scholar 

  • Nag A, Roy A, Dasgupta D (2015) An adaptive approach towards the selection of multi-factor authentication. In: 2015 IEEE symposium series on computational intelligence, pp 463–472

  • Parziale G, Chen Y (2009) Advanced technologies for touchless fingerprint recognition. In: Tistarelli M, Li SZ, Chellappa R (eds) Handbook of Remote Biometrics, ser. Advances in Pattern Recognition, Springer, London, pp 83–109

    Chapter  Google Scholar 

  • Patel VM, Yeh T, Fathy ME, Zhang Y, Chen Y, Chellappa R, Davis L (2013) Screen fingerprints: a novel modality for active authentication. IT Prof 15(4):38–42

    Article  Google Scholar 

  • Primo A, Phoha VV, Kumar R, Serwadda A (2014) Context-aware active authentication using smartphone accelerometer measurements. In: IEEE conference on computer vision and pattern recognition (CVPR) workshops

  • Razzaq A, Latif K, Ahmad HF, Hur A, Anwar Z, Bloodsworth PC (2014) Semantic security against web application attacks. Inf Sci 254:19–38

    Article  Google Scholar 

  • Ross TJ (2010) Fuzzy logic and engineering applications. Wiley, New Delhi

    Book  Google Scholar 

  • Roy A (2015) A novel multivariate fuzzy time series based forecasting algorithm incorporating the effect of clustering on prediction. Soft Comput. doi:10.1007/s00500-015-1619-3

  • Single-factor authentication (SFA) (2015). http://searchsecurity.techtarget.com/definition/single-factor-authentication-SFA

  • Serwadda A, Wang Z, Koch P, Govindarajan S, Pokala R, Goodkind A (2013) Scan-based evaluation of continuous keystroke authentication systems. IEEE IT Prof 15:20–23

    Article  Google Scholar 

  • Stewart JC, Monaco JV, Cha SH, Tappert CC (2011) An investigation of keystroke and Stylometry traits for authenticating online test takers. In: International joint conference on biometrics (IJCB). IEEE, pp 1–7

  • Tanaka K (1996) An introduction to fuzzy logic for practical applications. Springer, Berlin

    MATH  Google Scholar 

  • Tian Y, Kanade T, Cohn J (2000) Robust lip tracking by combining shape, color, and motion. In: Proceedings of ACCV’2000, pp 1040–1045

  • Tian YL, Kanade T, Cohn JF (2001)Recognizing facial actions by combining geometric features and regional appearance patterns. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, CMU-RI-TR-01-01, CMU

  • Vielhauer C (2006) Biometric user authentication for IT security. Springer, Berlin

    Google Scholar 

  • Zadeh LA (1975) The concept of linguistic variable and its application to approximate reasoning, parts 1–3. Inform Sci 8(3):199–249, 301–357, 9:43–80

  • Zi J, Dasgupta D (2009) V-detector: an efficient negative selection algorithm with “probably adequate” detector coverage. Inf Sci 179:1390–1406

    Article  Google Scholar 

  • Zimmermann HJ (1996) Fuzzy set theory and its applications. Allied, New Delhi

    Book  MATH  Google Scholar 

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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.

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Authors and Affiliations

  1. Department of Computer Science, University of Memphis, Memphis, TN, USA

    Arunava Roy & Dipankar Dasgupta

  2. Department of Industrial and Systems Engineering, National University of Singapore, Singapore, 117576, Singapore

    Arunava Roy

Authors
  1. Arunava Roy
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  2. Dipankar Dasgupta
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Correspondence to Arunava Roy.

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

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

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Communicated by V. Loia.

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Roy, A., Dasgupta, D. A fuzzy decision support system for multifactor authentication. Soft Comput 22, 3959–3981 (2018). https://doi.org/10.1007/s00500-017-2607-6

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