用Transformer生成网络时间序列数据，提升安全领域模型性能
Generative Modeling of Networked Time-Series via Transformer Architectures
- 基于Transformer设计生成框架，合成高质量时间序列样本
- 在多个数据集上达到当前最优（SOTA）生成效果
- 适用于安全与网络领域的机器学习流程增强
许多安全与网络应用需要大规模数据集来训练机器学习模型，但数据访问受限是安全领域长期存在的问题。近期研究显示，Transformer模型具备通过合成新样本扩大数据规模的潜力，但现有合成数据未能有效提升模型性能。为此，我们设计了一种高效的基于Transformer的生成模型，作为通用生成框架，可生成可用于提升现有及新机器学习工作流性能的时间序列数据。该模型具备良好的泛化能力，适用于多种数据集，生成样本质量高，并在多个任务中实现当前最优（SOTA）结果。
原文摘要 · Abstract (English)
Many security and network applications require having large datasets to train the machine learning models. Limited data access is a well-known problem in the security domain. Recent studies have shown the potential of Transformer models to enlarge the size of data by synthesizing new samples, but the synthesized samples don't improve the models over the real data. To address this issue, we design an efficient transformer-based model as a generative framework to generate time-series data, that can be used to boost the performance of existing and new ML workflows. Our new transformer model achieves the SOTA results. We style our model to be generalizable and work across different datasets, and produce high-quality samples.
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