Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview
Open accessAbstract
In recent years, we have seen an increase in computer attacks through our communication networks worldwide, whether due to cybersecurity systems’ vulnerability or their absence. This paper presents three quantum models to detect distributed denial of service attacks. We compare Quantum Support Vector Machines, hybrid QuantumClassical Neural Networks, and a two-circuit ensemble model running parallel on two quantum processing units. Our work demonstrates quantum models’ effectiveness in supporting current and future cybersecurity systems by obtaining performances close to 100%, being 96% the worst-case scenario. It compares our models’ performance in terms of accuracy and consumption of computational resources.
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Prior work has reviewed QSVM for DDoS detection and experimentally studied QSVM on NSL-KDD and NB-15 datasets, showing readiness for intrusion detection.
“For example, [17] reviews a quantum support vector machine (QSVM) constructed to detect DDoS attacks. [18] presents the experimental study of the QSVM running with conventional NSL-KDD and NB-15 datasets, that denotes the QSVM’s readiness to the intrusion detection on common attack datasets.”
From Security intrusion detection using quantum machine learning techniques (2022) · cites this paper as [17]
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- Authors
- Payares, E., & Martinez-Santos, J. C.
- Affiliations
- Universidad Tecnológica de Bolívar (CO)
- Corresponding author
- Payares, Esteban
- ORCID
- Payares, Esteban
- Venue
- Quantum Computing, Communication, and Simulation
- Publisher
- SPIE
- Published
- 2021-03-05
- Pages
- 47
- OpenAlex
- W3135681774
- Cited by
- 42
- References
- 18
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 8 · 2023: 10 · 2022: 4 · 2021: 1
- FWCI
- 5.32
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Quantum Computing, Communication, and Simulation (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- quantum machine learning, intrusion detection, DDoS attacks, quantum support vector machine, hybrid quantum-classical neural networks, cybersecurity, computational resource consumption
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