Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products
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Date
2024-11-29
Authors
Raghupathy, Bala Krishnan
Vairavasundram, Subramaniyaswamy
Ganesan, Manikandan
Namachivayam, Rajesh Kumar
Kotecha, Ketan
Herencsár, Norbert
ORCID
0000-0002-9504-2275Advisor
Referee
Mark
Journal Title
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Volume Title
Publisher
IEEE
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Abstract
Product tracking applications utilize the Internet of Things and cyber-physical systems to identify permitted or unauthorized user intrusions into the system. Classical machine learning algorithms cannot detect every risk in an environment that evolves constantly and where new abnormalities are visible. This article investigates the potential of quantum machine learning (QML) for real-time product purchase monitoring and intrusion detection using an enhanced quantum convolutional neural network (EQCNN) with signature-based detection over a massive volume of search space data (qubits). We suggest a three-stage technique to effectively handle the sensitive content: Pre-processing, EQCNN-based feature extraction, and syntactic pattern recognition. Signature-based identification is a feature of the EQCNN architecture that helps detect particular patterns linked to goods purchases or invasions. The model can minimize product tracking mistakes by utilizing the QML-based EQCNN with signature-based detection, resulting in a more efficient supply chain.
Description
Citation
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS. 2024, 13 p.
https://ieeexplore.ieee.org/document/10771968
https://ieeexplore.ieee.org/document/10771968
Document type
Peer-reviewed
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Accepted version
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Language of document
en
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Defence
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(C) IEEE