Leveraging Quantum Machine Learning for Intrusion Detection in Software-Defined Networks

Quantum machine learning (QML) algorithms for intrusion detection systems in software-defined networks are investigated, and their effectiveness is compared with their classical machine learning methods. The University of Nevada - Reno intrusion detection dataset (UNR-IDD) is used to evaluate different QML models, including quantum k-nearest neighbors (QKNNs), quantum support vector machines (QSVMs), quantum neural networks (QNNs), and hybrid quantum neural networks (HQNNs). These models were tested with quantum simulators to evaluate their potential advantages in processing complex datasets. The results show that HQNN and QSVM have higher accuracy than their classical SVM and NN counterparts. This study shows the potential of leveraging QML to enhance precision. References to other works that dive into efficiency and complexity are included.