用量子模型生成攻击数据，解决入侵检测中的样本不平衡问题。
Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing
- 在量子硬件上实现120×120的量子受限玻尔兹曼机，突破嵌入限制。
- 生成超160万条攻击样本，使数据集达420万条且平衡度显著提升。
- 生成样本质量优于传统方法，可大幅提升检测精度和召回率。
本研究探索在D-Wave Pegasus量子硬件上实现大规模量子受限玻尔兹曼机（QRBMs）作为生成模型，用于解决入侵检测系统（IDS）中的数据不平衡问题。借助Pegasus的增强连通性与计算能力，成功嵌入了包含120个可见单元和120个隐藏单元的QRBM，超越了默认嵌入工具的限制。该模型生成超过160万条攻击样本，构建了总规模超420万条记录的平衡数据集。与传统平衡方法（如SMOTE和RandomOversampler）相比，QRBM生成的合成样本质量更高，在多种分类器上显著提升了检测率、精确率、召回率和F1分数。研究证实了QRBM在可扩展性和效率方面的优势，任务完成时间仅需毫秒级。这些结果凸显了量子机器学习及QRBMs在数据预处理中的变革潜力，为现代信息系统中的复杂计算挑战提供了强大解决方案。
原文摘要 · Abstract (English)
This study explores the implementation of large Quantum Restricted Boltzmann Machines (QRBMs), a key advancement in Quantum Machine Learning (QML), as generative models on D-Wave's Pegasus quantum hardware to address dataset imbalance in Intrusion Detection Systems (IDS). By leveraging Pegasus's enhanced connectivity and computational capabilities, a QRBM with 120 visible and 120 hidden units was successfully embedded, surpassing the limitations of default embedding tools. The QRBM synthesized over 1.6 million attack samples, achieving a balanced dataset of over 4.2 million records. Comparative evaluations with traditional balancing methods, such as SMOTE and RandomOversampler, revealed that QRBMs produced higher-quality synthetic samples, significantly improving detection rates, precision, recall, and F1 score across diverse classifiers. The study underscores the scalability and efficiency of QRBMs, completing balancing tasks in milliseconds. These findings highlight the transformative potential of QML and QRBMs as next-generation tools in data preprocessing, offering robust solutions for complex computational challenges in modern information systems.
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