Leveraging quantum kernel methods for enhanced intrusion detection

Intrusion detection is essential for the security of modern networks against cyberattacks, but classical machine learning models like Support Vector Machines (SVM) often face challenges with high-dimensional data and scalability. In this work, we develop and explore the use of Quantum Support Vector Machines (QSVM) for intrusion detection using quantum kernels and feature maps to enhance classification accuracy. In this work, we compare the SVM and QSVM across four benchmark datasets, namely, CICIDS2017, NSL-KDD, CIDDS-001, and UGR’16 and also analyze the impact of applying Principal Component Analysis (PCA) for dimensionality reduction.Our results indicate that QSVM performs better in lower-dimensional spaces: on the CIDDS-001 dataset, QSVM achieved 97.1% accuracy compared to 94.5% for SVM, with a 3.2% increase in F1-score and 40% fewer false alarms. On the CICIDS2017 dataset, QSVM improved precision from 91% to 98% and reduced false positives by 98%, increasing overall reliability. Similarly, on the NSL-KDD dataset, QSVM achieved up to 1.5% higher accuracy and better F1-scores at moderate PCA dimensions (8-16) but lagged slightly in the lowest-dimensional case, suggesting its sensitivity to feature compression. However, for datasets with high dimensions like UGR’16, classical SVM maintained better performance, which shows the current limitations of QSVM scalability. Our study provides clear evidence that quantum-enhanced models can outperform classical SVMs under specific conditions, especially with reduced feature spaces.