Quantum Machine Learning for Anomaly Detection in 5G and beyond Mobile Networks

Enhancing anomaly detection in Network Intrusion Detection Systems (NIDS) is critical for the security and reliability of 5G networks. With the introduction of network slicing, Internet-of-Things (IoT) integration, and low-latency services, modern systems are confronted with a more complex attack surface, challenging traditional detection mechanisms. Hence, effective anomaly detection enables real-Time identification of suspicious activities, which is conventionally supported by machine learning (ML). However, ML struggles to detect threats in dynamic and heterogeneous environments. In this paper, we tackle this issue by leveraging quantum ML (QML). Specifically, we propose a novel QML-based NIDS supported by basic/Variational Autoencoders (VAEs), Principal Component Analysis (PCA), and dataset subsetting for features and dimensionality reduction. Through experiments, we demonstrate that our proposed QML framework effectively simplifies input data, optimizing the number of qubits and depth of quantum circuits required to address anomaly detection, resulting in faster processing, while maintaining a high cyberattack detection accuracy, compared to baselines such as Support Vector Machines (SVMs). Moreover, an impact analysis emphasizes the criticality of data distributions, data size, and class distributions on the QML-based NIDS performance, thus presenting novel guidelines for the efficient use of QML for anomaly detection.