Quantum Computing and Machine Learning for Cybersecurity: Distributed Denial of Service (DDoS) Attack Detection on Smart Micro-Grid

Dhaou Said ()
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Dhaou Said: Department of Electrical and Computer Engineering, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada

Energies, 2023, vol. 16, issue 8, 1-11

Abstract: Machine learning (ML) is efficiently disrupting and modernizing cities in terms of service quality for mobility, security, robotics, healthcare, electricity, finance, etc. Despite their undeniable success, ML algorithms need crucial computational efforts with high-speed computing hardware to deal with model complexity and commitments to obtain efficient, reliable, and resilient solutions. Quantum computing (QC) is presented as a strong candidate to help MLs reach their best performance especially for cybersecurity issues and digital defense. This paper presents quantum support vector machine (QSVM) model to detect distributed denial of service (DDoS) attacks on smart micro-grid (SMG). An evaluation of our approach against a real dataset of DDoS attack instances shows the effectiveness of our proposed model. Finally, conclusions and some open issues and challenges of the fitting of ML with QC are presented.

Keywords: quantum computing; quantum support vector machine; machine learning; digital defense; cybersecurity; support vector machine; distributed denial of service (DDoS) attacks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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