量子计算用于网络安全分类：近期分类与长期内存效率
Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency
- University of Pittsburgh（匹兹堡大学）
- University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文通过量子核SVM和量子预言草图两个实验，探讨量子计算在网络安全分类中的近期性能与长期内存效率优势，表明其价值在于内存高效的数据访问而非统一替代经典方法。
AI中文摘要：
量子计算已在多个网络安全应用中得到探索。然而，量子计算如何在近期和长期内为网络安全分类做出贡献尚未得到系统讨论。本文通过两个互补的实验研究这一问题。首先，我们在实际的网络安全分类任务上评估近期量子核支持向量机（SVMs），并将其与KDD Cup 1999、CICIDS2017和BoT-IoT上的经典SVM基线进行比较。在这些运行中，量子核具有竞争力。它们在某些设置中可以匹配或改进经典基线，而经典RBF核在其他设置中仍然更强。这表明近期量子核方法应被视为实用的、依赖数据集的经典核替代方案，而非统一优越的替代品。其次，我们使用量子预言草图（QOS）研究流式经典样本分类的长期内存优势。在QOS中，样本被在线处理并用于增量构建近似量子预言，该预言为下游量子算法提供相干查询访问，而无需保留整个数据集。在QOS启发的机器规模估计下，相当的精度对应于比显式稀疏/QRAM式存储小得多的有效内存规模代理。与简单的流式代理相比，结果更为细致，因为激进的特征过滤可以使流式维度变小。这表明量子计算对网络安全分类的长期价值可能在于内存高效的数据访问，而非即时运行速度提升。这些实验共同展示了量子计算如何从近期分类性能和长期内存效率两方面为网络安全分类做出贡献。
英文摘要：
Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-security classification in both the near term and the longer term has not been systematically discussed. This paper studies this question through two complementary experiments. First, we evaluate near-term quantum-kernel support vector machines (SVMs) on practical network-security classification tasks and compare them with classical SVM baselines on KDD Cup 1999, CICIDS2017, and BoT-IoT. Across these runs, quantum kernels are competitive. They can match or improve classical baselines in some settings, while classical RBF kernels remain stronger in others. This suggests that near-term quantum-kernel methods should be evaluated as practical, dataset-dependent alternatives to classical kernels rather than as uniformly superior replacements. Second, we use quantum oracle sketching (QOS) to study a longer-term memory advantage for classification with streaming classical samples. In QOS, samples are processed online and used to incrementally construct an approximate quantum oracle, which provides coherent query access for downstream quantum algorithms without retaining the entire dataset. Under the QOS-inspired machine-size estimate, comparable accuracy corresponds to a substantially smaller effective memory-size proxy than explicit sparse/QRAM-style storage. Compared with a simple streaming proxy, the result is more nuanced because aggressive feature filtering can make the streaming dimension small. This suggests that the long-term value of quantum computing for network-security classification may lie in memory-efficient data access rather than immediate runtime speedup. Together, these experiments show how quantum computing may contribute to network-security classification from near-term classification performance and longer-term memory efficiency.
