Modern cyberattacks are increasingly complex, posing significant challenges to classical machine learning methods, particularly when labeled data is limited and feature interactions are highly non-linear. In this study we investigates the potential of hybrid quantum-classical learning to enhance feature representations for intrusion detection and explore possible quantum advantages in cybersecurity analytics. Using the UNSW-NB15 dataset, network traffic is transformed into structured feature vectors through classical preprocessing and normalization. Classical models, including Logistic Regression and Support Vector Machines with linear and RBF kernels, are evaluated on the full dataset to establish baseline performance under large-sample conditions. Simultaneously, a quantum-enhanced pipeline maps classical features into variational quantum circuits via angle encoding and entangling layers, executed on a CPU-based quantum simulator, with resulting quantum embeddings classified using a classical SVM. Experiments show that while classical models achieve higher overall accuracy with large datasets, quantum-enhanced representations demonstrate superior attack recall and improved class separability when data is scarce, suggesting that quantum feature spaces capture complex correlations inaccessible to shallow classical models. These results highlight the potential of quantum embeddings to improve generalization and representation quality in cybersecurity tasks and provide a reproducible framework for evaluating quantum advantages as quantum hardware and simulators continue to advance.

翻译：现代网络攻击日益复杂，对经典机器学习方法构成重大挑战，特别是在标记数据有限且特征交互高度非线性的情况下。本研究探讨了混合量子-经典学习在增强入侵检测特征表示方面的潜力，并探索了量子计算在网络安全分析中可能带来的优势。利用UNSW-NB15数据集，通过网络流量经过经典预处理和归一化转换为结构化特征向量。在完整数据集上评估了包括逻辑回归和采用线性与RBF核的支持向量机在内的经典模型，以建立大样本条件下的基线性能。同时，构建了一个量子增强流程，通过角度编码和纠缠层将经典特征映射到变分量子电路中，该流程在基于CPU的量子模拟器上执行，生成的量子嵌入使用经典SVM进行分类。实验表明，虽然经典模型在大数据集上实现了更高的整体准确率，但在数据稀缺时，量子增强表示展现出更优的攻击召回率和改进的类别可分性，这表明量子特征空间能够捕获浅层经典模型无法获取的复杂关联。这些结果突显了量子嵌入在网络安全任务中提升泛化能力和表示质量的潜力，并为评估量子优势提供了一个可复现的框架，该框架将随着量子硬件和模拟器的持续发展而不断完善。