Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview

E. D. Payares; J. C. Martinez-Santos

doi:10.1117/12.2593297

5 March 2021 Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview

E. D. Payares, J. C. Martinez-Santos

Author Affiliations +

Proceedings Volume 11699, Quantum Computing, Communication, and Simulation; 116990B (2021) https://doi.org/10.1117/12.2593297
Event: SPIE OPTO, 2021, Online Only

Abstract

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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E. D. Payares and 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); https://doi.org/10.1117/12.2593297

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