Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview
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...
- Autores:
-
Payares, Esteban
Martínez-Santos, Juan Carlos
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/10426
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10426
- Palabra clave:
- Quantum computing
Quantum machine learning
Quantum Processing Units
Cybersecurity
DDoS attacks
Smart Intrusion Detection Systems
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | 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 Quantum- Classical Neural Networks, and a two-circuit ensemble model running parallel on two quantum processing units. Our work demonstrates quantum models' e ectiveness 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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