Reference Paper

papers/RefPaperByMarkyan04/Topic1 NetworkICS traffic generation/Flow-based Network Traffic Generation using Generative Adversarial Networks/Flow-based Network Traffic Generation using Generative Adversarial Ne.txt

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+@article{Ring_2019,
+   title={Flow-based network traffic generation using Generative Adversarial Networks},
+   volume={82},
+   ISSN={0167-4048},
+   url={http://dx.doi.org/10.1016/j.cose.2018.12.012},
+   DOI={10.1016/j.cose.2018.12.012},
+   journal={Computers & Security},
+   publisher={Elsevier BV},
+   author={Ring, Markus and Schlör, Daniel and Landes, Dieter and Hotho, Andreas},
+   year={2019},
+   month=may, pages={156–172} }

papers/RefPaperByMarkyan04/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。

papers/RefPaperByMarkyan04/Topic1 NetworkICS traffic generation/Practical GAN-based synthetic IP header trace generation using NetShare/Practical GAN-based synthetic IP header trace generation using NetShare.bib

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+@inproceedings{10.1145/3544216.3544251,
+author = {Yin, Yucheng and Lin, Zinan and Jin, Minhao and Fanti, Giulia and Sekar, Vyas},
+title = {Practical GAN-based synthetic IP header trace generation using NetShare},
+year = {2022},
+isbn = {9781450394208},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3544216.3544251},
+doi = {10.1145/3544216.3544251},
+abstract = {We explore the feasibility of using Generative Adversarial Networks (GANs) to automatically learn generative models to generate synthetic packet- and flow header traces for networking tasks (e.g., telemetry, anomaly detection, provisioning). We identify key fidelity, scalability, and privacy challenges and tradeoffs in existing GAN-based approaches. By synthesizing domain-specific insights with recent advances in machine learning and privacy, we identify design choices to tackle these challenges. Building on these insights, we develop an end-to-end framework, NetShare. We evaluate NetShare on six diverse packet header traces and find that: (1) across all distributional metrics and traces, it achieves 46\% more accuracy than baselines and (2) it meets users' requirements of downstream tasks in evaluating accuracy and rank ordering of candidate approaches.},
+booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference},
+pages = {458–472},
+numpages = {15},
+keywords = {generative adversarial networks, network flows, network packets, privacy, synthetic data generation},
+location = {Amsterdam, Netherlands},
+series = {SIGCOMM '22}
+}

papers/RefPaperByMarkyan04/Topic1 NetworkICS traffic generation/Realistic and responsive network traffic generation/Realistic and responsive network traffic generation.bib

@@ -0,0 +1,38 @@
+@article{10.1145/1151659.1159928,
+author = {Vishwanath, Kashi Venkatesh and Vahdat, Amin},
+title = {Realistic and responsive network traffic generation},
+year = {2006},
+issue_date = {October 2006},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+volume = {36},
+number = {4},
+issn = {0146-4833},
+url = {https://doi.org/10.1145/1151659.1159928},
+doi = {10.1145/1151659.1159928},
+abstract = {This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior. It then generates live traffic corresponding to the underlying models in a network emulation environment running commodity network protocol stacks. We find that the generated traces are statistically similar to the original traces. Further, to the best of our knowledge, we are the first to reproduce burstiness in traffic across a range of timescales using a model applicable to a variety of network settings. An initial sensitivity analysis reveals the importance of capturing and recreating user, application, and network characteristics to accurately reproduce such burstiness. Finally, we explore Swing's ability to vary user characteristics, application properties, and wide-area network conditions to project traffic characteristics into alternate scenarios.},
+journal = {SIGCOMM Comput. Commun. Rev.},
+month = aug,
+pages = {111–122},
+numpages = {12},
+keywords = {burstiness, energy plot, generator, internet, modeling, structural model, traffic, wavelets}
+}
+
+@inproceedings{10.1145/1159913.1159928,
+author = {Vishwanath, Kashi Venkatesh and Vahdat, Amin},
+title = {Realistic and responsive network traffic generation},
+year = {2006},
+isbn = {1595933085},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/1159913.1159928},
+doi = {10.1145/1159913.1159928},
+abstract = {This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior. It then generates live traffic corresponding to the underlying models in a network emulation environment running commodity network protocol stacks. We find that the generated traces are statistically similar to the original traces. Further, to the best of our knowledge, we are the first to reproduce burstiness in traffic across a range of timescales using a model applicable to a variety of network settings. An initial sensitivity analysis reveals the importance of capturing and recreating user, application, and network characteristics to accurately reproduce such burstiness. Finally, we explore Swing's ability to vary user characteristics, application properties, and wide-area network conditions to project traffic characteristics into alternate scenarios.},
+booktitle = {Proceedings of the 2006 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications},
+pages = {111–122},
+numpages = {12},
+keywords = {burstiness, energy plot, generator, internet, modeling, structural model, traffic, wavelets},
+location = {Pisa, Italy},
+series = {SIGCOMM '06}
+}
+

papers/RefPaperByMarkyan04/Topic1 NetworkICS traffic generation/Using GANs for Sharing Networked Time Series Data Challenges, Initial Promise, and Open Questions/Using GANs for Sharing Networked Time Series Da.txt

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+@inproceedings{Lin_2020, series={IMC ’20},
+   title={Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions},
+   url={http://dx.doi.org/10.1145/3419394.3423643},
+   DOI={10.1145/3419394.3423643},
+   booktitle={Proceedings of the ACM Internet Measurement Conference},
+   publisher={ACM},
+   author={Lin, Zinan and Jain, Alankar and Wang, Chen and Fanti, Giulia and Sekar, Vyas},
+   year={2020},
+   month=oct, pages={464–483},
+   collection={IMC ’20} }