Quantum machine learning for network intrusion systems: a systematic literature reiew


Quantum computing is a potential solution to several problems that classical computing faces such as computational time complexity. Quantum machine learning is therefore expected to have better runtime, capacity, and learning efficiency than classical methods offer. This article aims to present a systematic review of the state-of-the-art literature on quantum machine learning for cybersecurity in the specific application of network intrusion detection systems (IDS), identifying, analyzing, and correlating the different proposals for implementing quantum or hybrid algorithms. The methodology follows the Systematic Literature Review method, which, after its application, identified 5 articles that implemented quantum machine learning algorithms in the context of intrusion detection systems. The main algorithms were variational hybrid quantum-classical, with models based in quantum support vector machines and quantum neural networks. Benefits compared to purely classical models were observed and described, such as improved accuracy of attacking traffic data classification and reduced training time.

GAVA, Vagner Luiz; NICESIO, Otavio Kiyatake; LEAL Adriano Galido. Aprendizado de máquina quântico para sistemas de rede de detecção de intrusão, uma revisão sistemática da literatura. In: INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGY MANAGEMENT, 19.CONTECSI, 2022, São Paulo. Proceedings… São Paulo: FEA, 2023. 17p.

NICESIO, Otavio Kiyatake; LEAL, Adriano Galindo; GAVA, Vagner Luiz. Quantum machine learning for network instrusion detection systems, a systematic literature review. In: IEEE INTERNATIONAL CONFERENCE ON AI IN CYBERSECURITY, 2., 2023, Houston. Proceedings… 6p.

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