Xander: un ensamble cuántico-clásico para la detección de intrusiones avanzadas en redes modernas
DOI:
https://doi.org/10.59722/riic.v3i2.1233Palabras clave:
aprendizaje automático cuántico, ciberseguridad, modelos híbridos, NISQ, sistemas de detección de intrusionesResumen
La creciente complejidad de las amenazas cibernéticas exige sistemas de detección de intrusiones capaces de procesar tráfico de red dinámico y de alta dimensión. Aunque los métodos clásicos de aprendizaje automático han logrado un gran rendimiento en este ámbito, siguen enfrentándose a limitaciones relacionadas con la escalabilidad, la adaptabilidad y la robustez frente a los patrones de ataque en constante evolución. El aprendizaje automático cuántico se ha revelado como una alternativa prometedora; sin embargo, los procesadores cuánticos actuales, imponen limitaciones prácticas relacionadas con el ruido, la disponibilidad limitada de qubits, la decoherencia y la profundidad de los circuitos.Este artículo presenta a Xander, una arquitectura híbrida en ensamble cuántico-clásico para la detección de intrusiones en redes bajo las restricciones de la era NISQ. El marco propuesto integra modelos cuánticos, entre los que se incluyen componentes de máquinas de vectores de soporte cuánticos, clasificadores cuánticos variacionales y redes neuronales convolucionales cuánticas, con modelos clásicos como Random Forest, XGBoost y perceptrones multicapa.Estos modelos se combinan mediante un mecanismo optimizado de votación suave ponderada y se complementan con una estrategia de respaldo dinámica diseñada para reducir el impacto de los resultados cuánticos inestables. Los experimentos se llevaron a cabo utilizando subconjuntos reducidos y preprocesados de los conjuntos de datos CIC-IDS-2017 y CIC-IDS-2018, aplicando el análisis de componentes principales para permitir la codificación de características compatibles con la cuántica.El marco se evaluó mediante simulación clásica, simulación cuántica y ejecución en hardware cuántico real. Los resultados muestran que el enfoque híbrido alcanza un alto rendimiento de clasificación y mantiene un comportamiento estable bajo el ruido inducido por el hardware, alcanzando un rendimiento competitivo. Los hallazgos sugieren que los modelos híbridos constituyen una vía práctica para integrar la computación cuántica en aplicaciones de ciberseguridad sin requerir una ventaja cuántica a corto plazo.
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