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Developing intelligent cyber threat detection systems through quantum computing
1 Department of Mathematics, Lamar University, Beaumont, TX, USA.
2 Department of Computation Data Science and Engineering North Carolina A & T State University, Greensboro, NC, USA
3 Joint School of Nanoscience and Nanoengineering, North Carolina A&T States University, NC, USA.
4 Master of Business Administration (MBA) Program, University of North Alabama, Florence, AL USA.
5 Department of Industrial Engineering, Centre for Human Systems Engineering, University of Louisville, KY, USA.
6 School of International Business, Lincoln University, Oakland, CA, USA.
7 Department of Software Engineering, Wipro Technologies, Tallinn Estonia.
Research Article
International Journal of Science and Research Archive, 2024, 12(02), 1297–1307.
Article DOI: 10.30574/ijsra.2024.12.2.1369
Publication history:
Received on 17 June 2024; revised on 24 July 2024; accepted on 26 July 2024
Abstract:
In the face of increasingly sophisticated cyber threats, traditional detection systems often fall short in protecting critical supply chains. This research presents the development and evaluation of an intelligent cyber threat detection system integrating Quantum Computing (QC) and Artificial Intelligence (AI). The proposed system significantly enhances detection accuracy, reduces latency, and improves resource efficiency compared to traditional methods. Quantum algorithms, such as Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN), demonstrated superior performance with accuracies of 95.2% and 96.7%, respectively. The system achieved high detection rates for various cyber threats, including malware, phishing, ransomware, and advanced persistent threats (APTs), with reduced false positive rates. The integration of QC also resulted in faster threat detection and response times, with system latency halved across key components. These advancements provide substantial benefits for cyber threat response in supply chains, ensuring robust protection of financial transactions and critical infrastructure. The enhanced scalability and efficiency make the system a valuable asset for safeguarding the United States' financial sector against sophisticated cyber-attacks.
Keywords:
Quantum Computing; Artificial Intelligence; Cyber Threat Detection; Supply Chain Security; Quantum Support Vector Machines; Quantum Neural Networks
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
