Advancements in quantum machine learning for intrusion detection: A comprehensive overview
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Date
2023-09-07
Authors
Payares, Esteban
Martinez-Santos, Juan Carlos
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Abstract
This chapter provides a comprehensive overview of the recent developments in quantum machine
learning for intrusion detection systems. The authors review the state of the art based on the published
work “Quantum Machine Learning for Intrusion Detection of Distributed Denial of Service Attacks: A
Comparative View” and its relevant citations. The chapter discusses three quantum models, including
quantum support vector machines, hybrid quantum-classical neural networks, and a two-circuit ensemble
model, which run parallel on two quantum processing units. The authors compare the performance of
these models in terms of accuracy and computational resource consumption. Their work demonstrates
the effectiveness of quantum models in supporting current and future cybersecurity systems, achieving
close to 100% accuracy, with 96% being the worst-case scenario. The chapter concludes with future
research directions for this promising field.
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Citation
Payares, E. & Martinez-Santos, J. C. (2023). Advancements in Quantum Machine Learning for Intrusion Detection: A Comprehensive Overview. In N. Mateus-Coelho & M. Cruz-Cunha (Eds.), Exploring Cyber Criminals and Data Privacy Measures (pp. 167-176). IGI Global. https://doi.org/10.4018/978-1-6684-8422-7.ch009