LATIDIA · Ciberseguridad

Análisis comparativo del aprendizaje automático clásico y cuántico para la detección de intrusiones en múltiples conjuntos de datos

arXiv:2610.33119v1 Anuncio Tipo: nuevo Resumen: Se examina la detección de intrusiones mediante una evaluación comparativa de modelos de aprendizaje automático clásicos y cuánticos en tres conjuntos de datos: NSL-KDD, UNSW-NB15 y MQTTEEB-D2025. T

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arXiv:2610.33119v1 Announce Type: new Abstract: Intrusion detection is examined through a comparative benchmark of classical and quantum machine learning models on three datasets: NSL-KDD, UNSW-NB15, and MQTTEEB-D2025. The analysis focuses on how preprocessing decisions, feature selection strategies, and quantum encoding schemes shape model performance on data with differing levels of noise and complexity. A single unified pipeline is applied, covering normalization, class-imbalance handling, dimensionality reduction, and two feature selection methods: Random Forest importance and a quantum-aware approach that combines Quantum Kernel Alignment with Mutual Information (QKA-MI). Four classifiers are compared: Support Vector Machine, Random Forest, Quantum Support Vector Machine, and Pegasos Quantum SVM. The classical models perform consistently across all