Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection

TL;DR

This paper evaluates hybrid quantum-classical autoencoders for unsupervised network intrusion detection, demonstrating their potential to outperform classical models under certain conditions.

Contribution

It provides the first large-scale, data-driven analysis of HQC autoencoders for NIDS, exploring design choices and generalization capabilities.

Findings

HQC autoencoders match or exceed classical performance in key configurations.

Well-tuned HQC models offer stronger zero-day generalization.

Gate noise impacts early performance, highlighting the need for noise-aware designs.

Abstract

Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical (HQC) autoencoders for this task. We construct a unified experimental framework that iterates over key quantum design choices, including quantum-layer placement, measurement approach, variational and non-variational formulations, and latent-space regularization. Experiments across three benchmark NIDS datasets show that HQC autoencoders can match or exceed classical performance in their best configurations, although they exhibit higher sensitivity to architectural decisions. Under zero-day evaluation, well-configured HQC models provide stronger and more stable generalization than classical and supervised baselines. Simulated gate-noise experiments reveal early performance degradation, indicating the need for noise-aware HQC designs. These results provide the first data-driven characterization of HQC autoencoder behavior for network intrusion detection and outline key factors that govern their practical viability. All experiment code and configurations are available at https://github.com/arasyi/hqcae-network-intrusion-detection.