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 three datasets, whereas the quantum models depend more strongly on feature representation and kernel alignment. Quantum-aware feature selection yields marked gains for the quantum models, especially on cleaner datasets, while heterogeneous traffic remains difficult. Pegasos Quantum SVM offers a good trade-off between accuracy and computational cost, underscoring the role of preprocessing alignment in practical quantum intrusion detection.

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