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/
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Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview |
| title |
Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview |
| spellingShingle |
Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview Quantum computing Quantum machine learning Quantum Processing Units Cybersecurity DDoS attacks Smart Intrusion Detection Systems LEMB |
| title_short |
Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview |
| title_full |
Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview |
| title_fullStr |
Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview |
| title_full_unstemmed |
Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview |
| title_sort |
Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview |
| dc.creator.fl_str_mv |
Payares, Esteban Martínez-Santos, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Payares, Esteban Martínez-Santos, Juan Carlos |
| dc.subject.keywords.spa.fl_str_mv |
Quantum computing Quantum machine learning Quantum Processing Units Cybersecurity DDoS attacks Smart Intrusion Detection Systems |
| topic |
Quantum computing Quantum machine learning Quantum Processing Units Cybersecurity DDoS attacks Smart Intrusion Detection Systems LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
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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2021 |
| dc.date.issued.none.fl_str_mv |
2021-03-01 |
| dc.date.accessioned.none.fl_str_mv |
2022-01-28T20:08:39Z |
| dc.date.available.none.fl_str_mv |
2022-01-28T20:08:39Z |
| dc.date.submitted.none.fl_str_mv |
2022-01-28 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/restrictedAccess |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.identifier.citation.spa.fl_str_mv |
E. D. Payares, J. C. Martinez-Santos, "Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview," Proc. SPIE 11699, Quantum Computing, Communication, and Simulation, 116990B (5 March 2021); doi: 10.1117/12.2593297 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/10426 |
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10.1117/12.2593297 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
E. D. Payares, J. C. Martinez-Santos, "Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview," Proc. SPIE 11699, Quantum Computing, Communication, and Simulation, 116990B (5 March 2021); doi: 10.1117/12.2593297 10.1117/12.2593297 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/10426 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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11 Páginas |
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application/pdf |
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Cartagena de Indias |
| dc.source.spa.fl_str_mv |
Quantum Computing, Communication, and Simulation |
| institution |
Universidad Tecnológica de Bolívar |
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Payares, Estebane0aac5f6-3be2-4211-a602-9a0f32c4602dMartínez-Santos, Juan Carlosbf60b2aa-faf9-4266-b888-19ba6fe3d3492022-01-28T20:08:39Z2022-01-28T20:08:39Z2021-03-012022-01-28E. D. Payares, J. C. Martinez-Santos, "Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview," Proc. SPIE 11699, Quantum Computing, Communication, and Simulation, 116990B (5 March 2021); doi: 10.1117/12.2593297https://hdl.handle.net/20.500.12585/1042610.1117/12.2593297Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIn 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.11 Páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Quantum Computing, Communication, and SimulationQuantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overviewinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Quantum computingQuantum machine learningQuantum Processing UnitsCybersecurityDDoS attacksSmart Intrusion Detection SystemsLEMBCartagena de IndiasHavenstein, C., Thomas, D., and Chandrasekaran, S., \Comparisons of Performance between Quantum and Classical Machine Learning," SMU Data Science Review 1 (Jan. 2019).Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., and Lloyd, S., \Continuous-variable quantum neural networks," Physical Review Research 1, 033063 (Oct. 2019).Havl cek, V., C orcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., and Gambetta, J. M., \Supervised learning with quantum-enhanced feature spaces," Nature 567, 209{212 (Mar. 2019).Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., and Lloyd, S., \Quantum machine learning," Nature 549, 195{202 (Sept. 2017).Torlai, G. and Melko, R. G., \Machine-Learning Quantum States in the NISQ Era," Annual Review of Condensed Matter Physics 11, 325{344 (Mar. 2020).\The Quantum Computing Impact on Cybersecurity j QuantumXC."Mosca, M., \Cybersecurity in an era with quantum computers: Will we be ready?," IEEE Security Pri- vacy 16(5), 38{41 (2018).Abraham, H., AduO ei, Agarwal, R., Akhalwaya, I. Y., Aleksandrowicz, G., Alexander, T., Amy, M., Arbel, E., Arijit02, Asfaw, A., Avkhadiev, A., Azaustre, C., AzizNgoueya, Banerjee, A., Bansal, A., Barkoutsos, P., Barnawal, A., Barron, G., Barron, G. S., Bello, L., Ben-Haim, Y., Bevenius, D., Bhobe, A., Bishop, L. S., Blank, C., Bolos, S., Bosch, S., and et. al., \Qiskit: An open-source framework for quantum computing," (2019).Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Alam, M. S., Ahmed, S., Arrazola, J. M., Blank, C., Delgado, A., Jahangiri, S., McKiernan, K., Meyer, J. J., Niu, Z., Sz ava, A., and Killoran, N., \Penny- Lane: Automatic di erentiation of hybrid quantum-classical computations," arXiv:1811.04968 [physics, physics:quant-ph] (Feb. 2020). arXiv: 1811.04968.\DDoS 2019 j Datasets j Research j Canadian Institute for Cybersecurity j UNB."Abdi, H. and Williams, L. J., \Principal component analysis: Principal component analysis," Wiley Inter- disciplinary Reviews: Computational Statistics 2, 433{459 (July 2010).Lloyd, S., Schuld, M., Ijaz, A., Izaac, J., and Killoran, N., \Quantum embeddings for machine learning," (2020). arXiv: 2001.03622Suykens, J. and Vandewalle, J. Neural Processing Letters 9(3), 293{300 (1999).Rebentrost, P., Mohseni, M., and Lloyd, S., \Quantum Support Vector Machine for Big Data Classi cation," Physical Review Letters 113, 130503 (Sept. 2014).J., A., Adedoyin, A., Ambrosiano, J., Anisimov, P., B artschi, A., Casper, W., Chennupati, G., Co rin, C., Djidjev, H., Gunter, D., Karra, S., Lemons, N., Lin, S., Malyzhenkov, A., Mascarenas, D., Mniszewski, S., Nadiga, B., O'Malley, D., Oyen, D., Pakin, S., Prasad, L., Roberts, R., Romero, P., Santhi, N., Sinitsyn, N., Swart, P. J., Wendelberger, J. G., Yoon, B., Zamora, R., Zhu, W., Eidenbenz, S., Coles, P. J., Vu ray, M., and Lokhov, A. Y., \Quantum Algorithm Implementations for Beginners," arXiv:1804.03719 [quant-ph] (Mar. 2020). arXiv: 1804.03719.Abbas, A., Schuld, M., and Petruccione, F., \On quantum ensembles of quantum classi ers," arXiv:2001.10833 [quant-ph] (Jan. 2020). arXiv: 2001.10833.Broughton, M., Verdon, G., McCourt, T., Martinez, A. J., Yoo, J. H., Isakov, S. V., Massey, P., Niu, M. Y., Halavati, R., Peters, E., Leib, M., Skolik, A., Streif, M., Von Dollen, D., McClean, J. R., Boixo, S., Bacon, D., Ho, A. K., Neven, H., and Mohseni, M., \TensorFlow Quantum: A Software Framework for Quantum Machine Learning," arXiv:2003.02989 [cond-mat, physics:quant-ph] (Mar. 2020). arXiv: 2003.02989McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R., and Neven, H., \Barren plateaus in quantum neural network training landscapes," Nature Communications 9 (Nov. 2018).Schuld, M., Bocharov, A., Svore, K. M., and Wiebe, N., \Circuit-centric quantum classi ers," Physical Review A 101 (Mar 2020).http://purl.org/coar/resource_type/c_2df8fbb1ORIGINAL116990B.pdf116990B.pdfapplication/pdf6056111https://repositorio.utb.edu.co/bitstream/20.500.12585/10426/1/116990B.pdf4f3ccaad8c0f67b0c4705f6e5eb476ceMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/10426/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/10426/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXT116990B.pdf.txt116990B.pdf.txtExtracted texttext/plain26316https://repositorio.utb.edu.co/bitstream/20.500.12585/10426/4/116990B.pdf.txt930093766a64a56e4f89897e9c6fa7d7MD54THUMBNAIL116990B.pdf.jpg116990B.pdf.jpgGenerated 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