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internal-docs/papers/Topic1 NetworkICS traffic generation/Intro.txt
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网络流量/Trace 生成与“可用性”讨论(支撑你做语义 trace 生成,而不是原始字节生成)
Yin et al. Practical GAN-based Synthetic IP Header Trace Generation using NetShare. ACM SIGCOMM 2022.
用途:它强调“生成可用的协议字段 trace”与实用评估不是只看视觉相似。你可以借鉴其“字段级一致性/约束”的评估思路。
Lin et al. Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions. ACM IMC 2020.
用途:专门讨论网络化时间序列共享/合成的挑战(相关性、隐私、评估);你做 Modbus 合成的“评估指标设计”很适合引用它的观点。
Ring et al. Flow-based Network Traffic Generation using Generative Adversarial Networks. Computers & Security 2019.
用途:作为 GAN 基线类相关工作,对比扩散模型的训练稳定性与多样性优势。
Vishwanath & Vahdat. Swing: Realistic and Responsive Network Traffic Generation. IEEE/ACM ToN 2009.
用途:传统 traffic generator 经典工作;用于 related work 中“非深度学习合成”的对比。