arXiv：cs.LG· Boaz Micah, Nadia Milazzo, Maissa Beji, Borja Aizpurua, Lloren\c{c} Espinosa-Portal\'es, Esteban Payares, Ghada Ben Slama, Luc Andrea, Michel Kurek, Thomas Cope, Olivier Salomon·· 4 小时前AI 评分36
面向真实恶意软件信标数据的无监督异常检测：帕累托最优量子核选择
Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data
AI 导读
研究提出一种全无监督多目标协议，同时优化归一化伪差异（NPD）与相对经典参考核的几何差异（GD），并从帕累托前沿中选择模型，用于真实网络流量中的恶意软件信标检测。在 IQM 20 量子比特 Garnet 处理器上，NPD 引导选择找到的保真度核优于调优经典基线，但几何差异过小；帕累托选出的投影核 AUC 为 0.782，仅为略高于基线的 0.765。
正文
Authors:Boaz Micah, Nadia Milazzo, Maissa Beji, Borja Aizpurua, Llorenç Espinosa-Portalés, Esteban Payares, Ghada Ben Slama, Luc Andrea, Michel Kurek, Thomas Cope, Olivier Salomon
Abstract:Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quantities usually reported separately: how well a kernel performs on the task, and how far its geometry departs from the classical kernels available for the same problem. We introduce a fully unsupervised, multi-objective protocol that optimises simultaneously the normalised pseudo discrepancy (NPD), a label-free proxy for anomaly detection quality, and the geometric difference (GD) to a tuned classical reference kernel, selecting models from the resulting Pareto front. We apply it to malware beaconing detection in real network traffic, using a one-class support vector machine with fidelity and projected quantum kernels over four data encodings, on simulators and on IQM's 20-qubit Garnet processor. NPD-guided selection alone finds a fidelity kernel that beats the tuned classical baseline, but with a geometric difference too small to certify the gain as quantum. Projected kernels reach far larger geometric differences; the Pareto-selected one only marginally exceeds the baseline (AUC $0.782$ versus $0.765$, $g_{C\to Q}\approx 89>\sqrt{N}$ relative to that reference kernel), still below the NPD-selected fidelity kernel ($0.840$).
| Comments: | 13 pages, 5 figures |
| Subjects: | Quantum Physics (quant-ph); Cryptography and Security (cs.CR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09717 [quant-ph] |
| (or arXiv:2610.09717v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09717 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Llorenç Espinosa-Portalés [view email]
[v1]
Wed, 7 Oct 2026 09:13:12 UTC (1,042 KB)
来源：arXiv：cs.LG · arxiv.org