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Quantum Machine Learning and Fraud Detection

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Protocols, Strands, and Logic

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 13066))

Abstract

One of the most common problems in cybersecurity is related to the fraudulent activities that are performed in various settings and predominantly through the Internet. Securing online card transactions is a tough nut to crack for the banking sector, for which fraud detection is an essential measure. Fraud detection problems involve huge datasets and require fast and efficient algorithms. In this paper, we report on the use of a quantum machine learning algorithm for dealing with this problem and present the results of experimenting on a case study. By enhancing statistical models with the computational power of quantum computing, quantum machine learning promises great advantages for cybersecurity.

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Notes

  1. 1.

    For the purposes of this paper, it is sufficient to restrict ourselves to the finite-dimensional case where bounded linear operators can always be represented as matrices.

  2. 2.

    The security of RSA and Diffie-Hellman cannot be demonstrated anymore with the prospective use of a quantum computer, since efficient quantum algorithms exist for factorization and for computing the discrete logarithm.

  3. 3.

    The quantum computer currently available to the public is a very limited 16-qubit device, which is much too primitive (in terms of both stability and error tolerance) for practical problems.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università di Verona, Strada le Grazie 15, 34137, Verona, Italy

    Alessandra Di Pierro & Massimiliano Incudini

Authors
  1. Alessandra Di Pierro
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  2. Massimiliano Incudini
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Corresponding author

Correspondence to Alessandra Di Pierro .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA

    Daniel Dougherty

  2. Th.M. Siebel Center for Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA

    José Meseguer

  3. Institute of Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark

    Sebastian Alexander Mödersheim

  4. J83K, The MITRE Corporation, Bedford, MA, USA

    Paul Rowe

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Di Pierro, A., Incudini, M. (2021). Quantum Machine Learning and Fraud Detection. In: Dougherty, D., Meseguer, J., Mödersheim, S.A., Rowe, P. (eds) Protocols, Strands, and Logic. Lecture Notes in Computer Science(), vol 13066. Springer, Cham. https://doi.org/10.1007/978-3-030-91631-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-91631-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91630-5

  • Online ISBN: 978-3-030-91631-2

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