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引用次数: 0
摘要
近年来,网络领域的机器学习研究日趋活跃,对网络流量数据集的需求也随之增加。另一方面,作为机器学习的训练数据集,公开可用的网络流量数据集的数量和类型都很少。因此,我们将生成式对抗网络(GAN)作为数据生成模型,旨在使用生成的而非公开可用的训练数据集。然而,现有的生成式对抗网络难以生成足够多样化的网络流量,从而在代表工作日、周末和日期变化的同时提高泛化能力。本研究提出在条件 GAN 模型中插入一个新层,其功能是扩展时间序列流量数据的维度并嵌入时间位置信息。实验结果表明,插入了所建议的层的模型生成了代表时间特征的多样化网络流量数据。
Proposal of Temporal Feature Layers for Network Traffic Dataset Generation Using C-GAN
As machine learning research in the networking field has become more active in recent years, the demand for network traffic datasets has increased. On the other hand, the amount and types of publicly available network traffic datasets are scarce as training datasets for machine learning. Therefore, we focus on the generative adversarial network (GAN) as a data generation model, aiming to use generated rather than publicly available training datasets. However, existing GANs have difficulty generating sufficiently diverse network traffic to improve generalization ability while representing variations across weekdays, weekends, and date. This study proposes a new layers inserted into the conditional GAN model with the functions of expanding dimensionality of time-series traffic data and embedding temporal position information. Experimental results show that the model with the proposed layers inserted generated diverse network traffic data that represents temporal features.