使用GNN驱动的GAN生成数据包级头部轨迹
Generating Packet-Level Header Traces Using GNN-powered GAN
AI总结：
本文提出结合GNN与GAN并引入word2vec嵌入生成数据包级头部轨迹，缓解独热编码的维度灾难，提升生成数据的真实性、多样性与判别能力，为网络流量数据合成提供新方法。
摘要：
本研究提出了一种结合图神经网络（GNN）和生成对抗网络（GAN）的新方法，用于生成数据包级头部轨迹。通过引入word2vec嵌入，本工作显著缓解了传统独热编码常伴随的维度灾难问题，从而提升了模型的训练效果。实验结果表明，word2vec编码比独热编码更有效地捕捉字段值之间的语义关系，提高了生成数据的准确性和自然度。此外，GNN的引入进一步增强了判别器区分真实数据与合成数据的能力，从而生成更真实、更多样化的样本。这些发现不仅为网络流量数据生成提供了新的理论方法，也为通过改进特征表示和模型架构来提升数据合成质量提供了实践启示。未来研究可聚焦于优化GNN与GAN的集成、降低计算成本，并在更大规模数据集上验证模型的泛化能力。探索其他编码方法和模型结构改进也可能为网络数据生成带来新的可能性。本研究推动了数据合成领域的发展，在网络流量分析和安全领域具有潜在应用价值。
英文摘要：
This study presents a novel method combining Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) for generating packet-level header traces. By incorporating word2vec embeddings, this work significantly mitigates the dimensionality curse often associated with traditional one-hot encoding, thereby enhancing the training effectiveness of the model. Experimental results demonstrate that word2vec encoding captures semantic relationships between field values more effectively than one-hot encoding, improving the accuracy and naturalness of the generated data. Additionally, the introduction of GNNs further boosts the discriminator's ability to distinguish between real and synthetic data, leading to more realistic and diverse generated samples. The findings not only provide a new theoretical approach for network traffic data generation but also offer practical insights into improving data synthesis quality through enhanced feature representation and model architecture. Future research could focus on optimizing the integration of GNNs and GANs, reducing computational costs, and validating the model's generalizability on larger datasets. Exploring other encoding methods and model structure improvements may also yield new possibilities for network data generation. This research advances the field of data synthesis, with potential applications in network security and traffic analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
