发表机构
Hamdard University; SZABIST University; Fandaqah（哈姆德大学; SZABIST大学; 范达卡）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出公平的量子机器学习网络入侵检测基准与归因审计，发现调优后的经典模型在多数数据集上优于量子模型，仅量子核SVM在两项指标、小型四量子比特模型在特定任务上优于经典基线，贡献不受量子模型胜负影响。
AI 中文摘要
网络入侵检测的量子机器学习（QML）常被报道达到近乎完美的准确率，但最严谨的研究发现，调优得当的经典模型仍具竞争力，且表观的量子增益可能是经典降维与隐式正则化的人工产物，而非真正的量子效应。本研究不关注量子模型能否达到高准确率，而是探究其优势的量子属性究竟有多强。我们提出了一个统一、可复现的QML-IDS基准，在一个泄漏控制协议、等预算特征视图、不平衡与校准感知指标及显著性检验下，针对四个标准NIDS数据集（NSL-KDD、UNSW-NB15、CICIDS2017、NF-ToN-IoT-v2），将混合变分量子电路与量子核支持向量机（SVM）与五个经诚实调优的经典基线进行评估；还引入了量子归因审计（含参数匹配的经典对照、随机特征核及正则化扫描），以量化任何增益中有多少真正可归因于量子组件。调优后的经典模型（随机森林、XGBoost）在所有数据集的总体检测上均与量子模型相当或优于后者，审计将此归因于经典预处理与正则化，而非量子效应。经错误发现率校正后仍保留两个优势：量子核SVM在AUPRC与ROC-AUC上优于其直接经典替代模型（随机特征核），且一个小型四量子比特混合模型在分布偏移的NSL-KDD任务上，于1%误报操作点的检测性能优于最佳经典基线（p=0.005，BH q=0.030）。代码、种子与数据划分已公开；无论量子模型胜出、持平或落败，本研究的贡献均成立。
英文摘要
Quantum machine learning (QML) for network intrusion detection (NIDS) is routinely reported to reach near-perfect accuracy, yet the most rigorous studies find that well-tuned classical models remain competitive, and that apparent quantum gains may be artefacts of classical dimensionality reduction and implicit regularisation rather than genuine quantum effects. We ask not whether a quantum model can post a high accuracy, but how quantum the advantage really is. We present a unified, reproducible QML-IDS benchmark evaluating hybrid variational quantum circuits and quantum-kernel SVMs against five honestly-tuned classical baselines across four standard NIDS datasets (NSL-KDD, UNSW-NB15, CICIDS2017, NF-ToN-IoT-v2) under one leakage-controlled protocol, with an equal-budget feature view, imbalance- and calibration-aware metrics with significance testing, and a simulated NISQ noise sweep. We introduce a quantum-attribution audit (parameter-matched classical controls, a random-feature kernel, and a regularisation sweep) that quantifies how much of any gain is genuinely attributable to the quantum component. Tuned classical models (Random Forest, XGBoost) match or exceed the quantum models on aggregate detection on every dataset, and the audit attributes this to classical preprocessing and regularisation rather than quantum effects. Two advantages survive false-discovery-rate correction: the quantum-kernel SVM out-ranks its direct classical surrogate (a random-feature kernel) on AUPRC and ROC-AUC, and a small four-qubit hybrid out-detects the best classical baseline at the 1% false-positive operating point on the distribution-shifted NSL-KDD task (p = 0.005, BH q = 0.030). Code, seeds, and splits are released; our contribution stands whether quantum wins, ties, or loses.
Comments19 pages, 4 figures, 5 tables
