发表机构
Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出混合量子-经典架构，在资源受限下用QSVM和VQC量子头提升威胁检测，仿真与IBM硬件验证显示其性能匹配经典模型，并减少近边界误报漏报。
AI 中文摘要
在特征和计算预算紧张的情况下，经典威胁检测流程在接近决策边界的事件上往往性能下降。小型量子处理器现已可用，但现有工作未能充分证明在上述资源受限条件下，量子组件能否提升端到端威胁检测性能。本文旨在通过一种混合架构来弥补这一空白，该架构使用紧凑的多层感知机层压缩数据信息，然后将处理后的特征路由到由量子支持向量机（QSVM）和变分量子电路（VQC）模型实现的少量量子比特量子头。在仿真平台上，我们将这些混合模型与具有可比参数预算的经典模型在两个代表性网络安全任务上进行基准测试：基于NSL-KDD数据集的网络入侵检测和基于Ling-Spam数据集的垃圾邮件过滤。为验证其在真实量子硬件上的精度，我们将最佳4量子比特QSVM模型部署在IBM量子设备上，采用噪声感知执行，并在较小的子数据集上进行评估。结果表明，浅层量子头在性能上持续与使用相同特征的经典模型持平，并且在困难的近边界情况下适度减少了漏报攻击和误报。硬件验证结果与仿真行为高度吻合，剩余差距主要由设备噪声而非模型设计主导。此外，我们进行了对抗攻击以测试一个QSVM模型的鲁棒性。综合来看，研究表明即使在小型、有噪声的设备上，精心设计的量子组件也能在实际网络威胁检测应用中作为具有竞争力且预算感知的组件发挥作用。
英文摘要
Under tight feature and compute budgets, classical threat detection pipelines often degrade on near-decision-boundary events. Small quantum processors are now available, but existing work inadequately shows whether quantum components improve end-to-end threat detection under the above resource constrained conditions. This paper tries to address this gap with a hybrid architecture that uses a compact multilayer perceptron layer to compress the information in data and then routes the processed features to a few qubit quantum heads implemented in quantum support vector machine (QSVM) and variational quantum circuit (VQC) models. On a simulation platform, we benchmark these hybrid models against classical models with comparable parameter budgets on two representative cybersecurity tasks, network intrusion detection on NSL-KDD dataset and spam filtering on Ling-Spam dataset. To validate their precision on real quantum hardware, we deploy the best 4-qubit QSVM model on an IBM Quantum device with noise-aware execution, evaluated on a smaller sub-dataset. In the results, shallow quantum heads consistently match, and on difficult near-boundary cases modestly reduce missed attacks and false alarms compared to classical models using the same features. Hardware validation results track the simulation behavior closely enough that the remaining gap is dominated by device noise rather than model design. Furthermore, we conduct adversarial attacks to test the robustness of one QSVM model. Taken together, the study shows that even on small, noisy devices, carefully engineered quantum components may function as competitive, budget-aware components in practical cyber threat detection applications.
