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 data sets 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.

Quantum Machine Learning and Fraud Detection

Alessandra Di Pierro;Massimiliano Incudini
2021-01-01

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 data sets 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.
978-3-030-91630-5
Quantum Computing, Support Vector Machine, Cybersecurity and Fraud
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1057547
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