Advancements in quantum machine learning for intrusion detection: A comprehensive overview

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.

• Autores: Payares, Esteban; Martinez-Santos, Juan Carlos
• Fecha de publicación: 2023
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/12587
• Acceso en línea: https://hdl.handle.net/20.500.12585/12587
• Palabra clave: Quantum Machine Learning; Machine Learning; Quantum Computing; LEMB
• Rights: openAccess
• License: http://purl.org/coar/access_right/c_abf2