电力系统攻击检测的量子机器学习基准测试：评估选择在模型之前决定结果
Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Do
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中文总结 AI 辅助
该研究针对电力系统攻击检测，将量子机器学习模型与经典模型在多类攻击下对比，发现评估选择会影响结果，发布了设种子的基准测试。
中文摘要 AI 辅助
电力系统网络攻击的机器学习检测器本身就是攻击面，因此有研究提出将量子机器学习应用于该领域。我们在公共电力系统攻击数据（密西西比州立大学/ORNL）上，针对白盒、迁移、基于决策的黑盒及投毒攻击，将保真度核支持向量机（fidelity-kernel SVM）和变分类器与6个经过调优的经典模型进行基准测试。我们的核心发现是方法论层面的：基准测试的结果由评估者的选择决定，而非模型本身。基准测试中有8项选择——6项属于评估协议、2项属于基准测试自身的调优——每一项都会在固定模型下反转或改变结论。最大的差异来自数据划分：行级协议的宏F1得分为0.905，而保留整个源文件的划分方式仅为0.594；在受限匹配维度 regime 下，量子模型组的表现处于随机噪声范围内，经典模型组则高出0.024。保真度核在未受直接攻击时表现最稳健（保留率从0.886降至0.064）；拟合不当的代理会产生10倍的不对称性；未设种子的黑盒攻击在重启间会产生75%的变动。一项阳性对照解释了精度无效性的原因：是标签而非管道导致的。我们提供了可捕捉每项选择的对照项，并发布了设种子的基准测试。
英文摘要
Machine-learning detectors for power-system cyberattacks are themselves attack surfaces, and quantum machine learning has been proposed for them. We benchmark fidelity-kernel SVMs and variational classifiers against six tuned classical models on public power-system attack data (Mississippi State/ORNL), across white-box, transfer, decision-based black-box, and poisoning attacks. Our headline finding is methodological: the benchmark's answers are set by the evaluator's choices before the models. Eight choices -- six in the evaluation protocol, two in the tuning the benchmark itself runs -- each reversed or moved a conclusion at fixed models. The largest is the split: the row-level protocol scores 0.905 macro-F1 where holding whole source files out leaves 0.594, and in the capped matched-dimensionality regime the quantum arm sits within noise of chance with the classical arm 0.024 above it. A fidelity kernel looks most robust until attacked directly (retention 0.886 to 0.064); a mis-fitted surrogate manufactures a 10x asymmetry; an unseeded black-box attack moves 75% between restarts. A positive control explains the accuracy null: the labels, not the pipeline. We give the control that catches each choice and release the seeded benchmark.
