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

@@ -0,0 +1,13 @@
+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

@@ -0,0 +1,17 @@
+@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

@@ -0,0 +1,10 @@
+@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} }

papers/RefPaperByMarkyan04/Topic2 Protocol-aware generation & fuzzing/AFLNet Five Years Later On Coverage-Guided Protocol Fuzzing/AFLNet Five Years Later On Coverage-Guided Protocol Fuzzing.txt

@@ -0,0 +1,9 @@
+@misc{meng2025aflnetyearslatercoverageguided,
+      title={AFLNet Five Years Later: On Coverage-Guided Protocol Fuzzing}, 
+      author={Ruijie Meng and Van-Thuan Pham and Marcel Böhme and Abhik Roychoudhury},
+      year={2025},
+      eprint={2412.20324},
+      archivePrefix={arXiv},
+      primaryClass={cs.SE},
+      url={https://arxiv.org/abs/2412.20324}, 
+}

papers/RefPaperByMarkyan04/Topic2 Protocol-aware generation & fuzzing/Intro.txt

@@ -0,0 +1,17 @@
+协议状态机/模糊测试/学习输入生成（支撑你“生成有效 request-response 交互序列”）
+
+对 Modbus TCP 来说，“有效”不仅是字段合法，还包括：
+request 与 response 配对
+Transaction ID 一致/递增策略合理
+功能码与地址范围一致（如 0x03 对 holding register 区间）
+异常响应的触发条件合理
+这些强约束往往在 fuzzing / protocol testing 文献里讨论得更系统。
+
+Pham et al. AFLNet: A Greybox Fuzzer for Network Protocols. ICST 2019.
+用途：面向网络协议的状态覆盖 fuzzing；你可以借鉴其“状态反馈”思想，把扩散生成器和协议栈反馈（有效率/覆盖率）结合起来做强化。
+
+She et al. NEUZZ: Efficient Fuzzing with Neural Networks. IEEE S&P 2019.
+用途：神经网络引导 fuzzing 的代表作；可作为你未来“生成模型 + 反馈优化/引导采样”的相关工作支撑。
+
+Godefroid, Peleg, Singh. Learn&Fuzz: Machine Learning for Input Fuzzing. ASE 2017.
+用途：学习输入格式再生成；与你“语义级生成 + 确定性组装器”的理念一致（模型学语义，规则负责封包细节）。

papers/RefPaperByMarkyan04/Topic2 Protocol-aware generation & fuzzing/Learn&Fuzz Machine Learning for Input Fuzzing/Learn&Fuzz Machine Learning for Input Fuzzing.bib

@@ -0,0 +1,9 @@
+@misc{godefroid2017learnfuzzmachinelearninginput,
+      title={Learn&Fuzz: Machine Learning for Input Fuzzing}, 
+      author={Patrice Godefroid and Hila Peleg and Rishabh Singh},
+      year={2017},
+      eprint={1701.07232},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/1701.07232}, 
+}

papers/RefPaperByMarkyan04/Topic2 Protocol-aware generation & fuzzing/NEUZZ Efficient Fuzzing with Neural Program Smoothing/NEUZZ Efficient Fuzzing with Neural Program Smoothing.bib

@@ -0,0 +1,9 @@
+@misc{she2019neuzzefficientfuzzingneural,
+      title={NEUZZ: Efficient Fuzzing with Neural Program Smoothing}, 
+      author={Dongdong She and Kexin Pei and Dave Epstein and Junfeng Yang and Baishakhi Ray and Suman Jana},
+      year={2019},
+      eprint={1807.05620},
+      archivePrefix={arXiv},
+      primaryClass={cs.CR},
+      url={https://arxiv.org/abs/1807.05620}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting/Autoregressive Denoising Diffusion.bib

@@ -0,0 +1,9 @@
+@misc{rasul2021autoregressivedenoisingdiffusionmodels,
+      title={Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting}, 
+      author={Kashif Rasul and Calvin Seward and Ingmar Schuster and Roland Vollgraf},
+      year={2021},
+      eprint={2101.12072},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2101.12072}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/CSDI Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation/CSDI Conditional Score-based Diffusion Model.bib

@@ -0,0 +1,9 @@
+@misc{tashiro2021csdiconditionalscorebaseddiffusion,
+      title={CSDI Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation}, 
+      author={Yusuke Tashiro and Jiaming Song and Yang Song and Stefano Ermon},
+      year={2021},
+      eprint={2107.03502},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={httpsarxiv.orgabs2107.03502}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/Denoising Diffusion Probabilistic Models/Denoising Diffusion Probabilistic Models.bib

@@ -0,0 +1,11 @@
+@inproceedings{NEURIPS2020_4c5bcfec,
+ author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+ booktitle = {Advances in Neural Information Processing Systems},
+ editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
+ pages = {6840--6851},
+ publisher = {Curran Associates, Inc.},
+ title = {Denoising Diffusion Probabilistic Models},
+ url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
+ volume = {33},
+ year = {2020}
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/DiffSTG Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models/DiffSTG Probabilistic Spatio-Temporal Graph.bib

@@ -0,0 +1,9 @@
+@misc{wen2024diffstgprobabilisticspatiotemporalgraph,
+      title={DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models}, 
+      author={Haomin Wen and Youfang Lin and Yutong Xia and Huaiyu Wan and Qingsong Wen and Roger Zimmermann and Yuxuan Liang},
+      year={2024},
+      eprint={2301.13629},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2301.13629}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/DiffWave A Versatile Diffusion Model for Audio Synthesis/DiffWave A Versatile Diffusion Model for Audio Synthesis.bib

@@ -0,0 +1,9 @@
+@misc{kong2021diffwaveversatilediffusionmodel,
+      title={DiffWave: A Versatile Diffusion Model for Audio Synthesis}, 
+      author={Zhifeng Kong and Wei Ping and Jiaji Huang and Kexin Zhao and Bryan Catanzaro},
+      year={2021},
+      eprint={2009.09761},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS},
+      url={https://arxiv.org/abs/2009.09761}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/Intro.txt

@@ -0,0 +1,22 @@
+扩散模型（DDPM/Score）用于时间序列/时空建模（最直接支撑你“用 diffusion 生成包序列”）
+
+Ho, Jain, Abbeel. Denoising Diffusion Probabilistic Models (DDPM). NeurIPS 2020.
+用途：扩散模型基本形式（前向加噪、反向去噪、预测噪声训练）。你方法部分的扩散理论根引用。
+
+Song et al. Score-Based Generative Modeling through Stochastic Differential Equations. ICLR 2021.
+用途：score-based diffusion 的更一般表述；如果你未来要做连续时间（时间间隔/抖动）的建模，这条线很有用。
+
+Rasul et al. Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. ICML 2021.
+用途：多变量时间序列的扩散建模；对应你“多个(设备,寄存器)序列”的联合分布生成。
+
+Tashiro et al. CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. NeurIPS 2021.
+用途：条件扩散（conditioning）注入方式很适合你：把设备嵌入/寄存器语义/主从角色/工艺状态作为条件，约束生成。
+
+Liu et al. PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation. ICDE 2023.
+用途：时空条件扩散框架；你把“空间”换成(设备,寄存器)二部图/异构图，“时间”换成轮询/会话位置，结构很贴近。
+
+Wen et al. DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models. ACM SIGSPATIAL 2023.
+用途：扩散 + 时空图；你做(设备,寄存器)图上的生成（而不是预测）时，可借鉴其图特征融入去噪网络的方式。
+
+Kong et al. DiffWave: A Versatile Diffusion Model for Audio Synthesis. ICLR 2021.
+用途：一维信号生成（类似“时间间隔序列”“值序列”）；其 WaveNet/UNet 类去噪骨架对工业轮询类高频序列也很参考。

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/PriSTI A Conditional Diffusion Framework for Spatiotemporal Imputation/PriSTI A Conditional Diffusion Framework for Spatiotemporal .bib

@@ -0,0 +1,9 @@
+@misc{liu2023pristiconditionaldiffusionframework,
+      title={PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation}, 
+      author={Mingzhe Liu and Han Huang and Hao Feng and Leilei Sun and Bowen Du and Yanjie Fu},
+      year={2023},
+      eprint={2302.09746},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2302.09746}, 
+}

papers/RefPaperByMarkyan04/Topic3 Diffusion for time series or spatiotemporal/Score-Based Generative Modeling through Stochastic Differential Equations/Score-Based Generative Modeling through Stochastic Differ.bib

@@ -0,0 +1,9 @@
+@misc{song2021scorebasedgenerativemodelingstochastic,
+      title={Score-Based Generative Modeling through Stochastic Differential Equations}, 
+      author={Yang Song and Jascha Sohl-Dickstein and Diederik P. Kingma and Abhishek Kumar and Stefano Ermon and Ben Poole},
+      year={2021},
+      eprint={2011.13456},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2011.13456}, 
+}

papers/RefPaperByMarkyan04/Topic4 Discrete diffusion and mixed-type generation/Diffusion-LM Improves Controllable Text Generation/Diffusion-LM Improves Controllable Text Generation.bib

@@ -0,0 +1,9 @@
+@misc{li2022diffusionlmimprovescontrollabletext,
+      title={Diffusion-LM Improves Controllable Text Generation}, 
+      author={Xiang Lisa Li and John Thickstun and Ishaan Gulrajani and Percy Liang and Tatsunori B. Hashimoto},
+      year={2022},
+      eprint={2205.14217},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={httpsarxiv.orgabs2205.14217}, 
+}

papers/RefPaperByMarkyan04/Topic4 Discrete diffusion and mixed-type generation/Intro.txt

@@ -0,0 +1,17 @@
+离散/混合变量扩散（解决 Modbus 的功能码/地址等离散字段）
+
+你是“语义级生成”：至少包含
+离散：Function Code、（可选）异常码、寄存器地址/地址簇、读写长度
+连续/整数：寄存器值、时间间隔（inter-arrival）
+这类“混合类型生成”往往要引用离散扩散或 embedding trick。
+
+Austin et al. Structured Denoising Diffusion Models in Discrete State-Spaces (D3PM). NeurIPS 2021.
+用途：离散状态空间扩散；功能码/异常码/地址簇（token）可以用 D3PM 直接扩散生成。
+
+Li et al. Diffusion-LM Improves Controllable Text Generation. NeurIPS 2022.
+用途：文本是离散序列生成；你可将“PDU 字段序列/行为序列”类比为句子，并用其“可控生成”的讨论支撑“协议约束/场景约束”的必要性。
+
+工程落地常见做法：
+离散字段：D3PM/Multinomial diffusion 或先 embedding 到连续空间再做高斯扩散；
+连续字段：标准 DDPM；
+最后用一个 deterministic assembler（pymodbus/scapy）组装 MBAP/PDU，确保协议有效率接近 100%。

papers/RefPaperByMarkyan04/Topic4 Discrete diffusion and mixed-type generation/Structured Denoising Diffusion Models in Discrete State-Spaces/Structured Denoising Diffusion Models in Discrete State-Spaces.bib

@@ -0,0 +1,9 @@
+@misc{austin2023structureddenoisingdiffusionmodels,
+      title={Structured Denoising Diffusion Models in Discrete State-Spaces}, 
+      author={Jacob Austin and Daniel D. Johnson and Jonathan Ho and Daniel Tarlow and Rianne van den Berg},
+      year={2023},
+      eprint={2107.03006},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2107.03006}, 
+}

papers/RefPaperByMarkyan04/Topic5 Graph or heterogeneous graph priors/Graph Attention Networks/Graph Attention Networks.bib

@@ -0,0 +1,9 @@
+@misc{veličković2018graphattentionnetworks,
+      title={Graph Attention Networks}, 
+      author={Petar Veličković and Guillem Cucurull and Arantxa Casanova and Adriana Romero and Pietro Liò and Yoshua Bengio},
+      year={2018},
+      eprint={1710.10903},
+      archivePrefix={arXiv},
+      primaryClass={stat.ML},
+      url={https://arxiv.org/abs/1710.10903}, 
+}

papers/RefPaperByMarkyan04/Topic5 Graph or heterogeneous graph priors/GraphMAE Self-Supervised Masked Graph Autoencoders/GraphMAE Self-Supervised Masked Graph Autoencoders.bib

@@ -0,0 +1,9 @@
+@misc{hou2022graphmaeselfsupervisedmaskedgraph,
+      title={GraphMAE: Self-Supervised Masked Graph Autoencoders}, 
+      author={Zhenyu Hou and Xiao Liu and Yukuo Cen and Yuxiao Dong and Hongxia Yang and Chunjie Wang and Jie Tang},
+      year={2022},
+      eprint={2205.10803},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2205.10803}, 
+}

papers/RefPaperByMarkyan04/Topic5 Graph or heterogeneous graph priors/Heterogeneous Graph Transformer/Heterogeneous Graph Transformer.bib

@@ -0,0 +1,9 @@
+@misc{hu2020heterogeneousgraphtransformer,
+      title={Heterogeneous Graph Transformer}, 
+      author={Ziniu Hu and Yuxiao Dong and Kuansan Wang and Yizhou Sun},
+      year={2020},
+      eprint={2003.01332},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2003.01332}, 
+}

papers/RefPaperByMarkyan04/Topic5 Graph or heterogeneous graph priors/How Powerful are Graph Neural Networks/How Powerful are Graph Neural Networks.bib

@@ -0,0 +1,9 @@
+@misc{xu2019powerfulgraphneuralnetworks,
+      title={How Powerful are Graph Neural Networks?}, 
+      author={Keyulu Xu and Weihua Hu and Jure Leskovec and Stefanie Jegelka},
+      year={2019},
+      eprint={1810.00826},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1810.00826}, 
+}

papers/RefPaperByMarkyan04/Topic5 Graph or heterogeneous graph priors/Intro.txt

@@ -0,0 +1,21 @@
+图表示学习/异构图（直接对应你选的(设备,寄存器)结构）
+
+你明确了节点是 (设备,寄存器)，这天然是二部图/异构图（Device 与 Register 两类实体）。STOUTER 用的是 base station graph；你这里更建议引用异构图建模经典方法，解释为什么要用二部图结构做“空间先验”。
+
+Kipf & Welling. Semi-Supervised Classification with Graph Convolutional Networks (GCN). ICLR 2017.
+用途：基础 GCN；你做拓扑表征学习的常用基线引用。
+
+Veličković et al. Graph Attention Networks (GAT). ICLR 2018.
+用途：注意力聚合，适合“不同邻边权重不同”（例如不同设备之间依赖强弱不同）。
+
+Xu et al. How Powerful are Graph Neural Networks? (GIN). ICLR 2019.
+用途：强调结构表达能力；如果你需要强结构区分能力，可引用。
+
+Schlichtkrull et al. Modeling Relational Data with Graph Convolutional Networks (R-GCN). ESWC 2018.
+用途：关系类型图卷积；很适合你在(设备,寄存器)图里加入多种边：read、write、same-device、process-link 等关系类型。
+
+Hu et al. Heterogeneous Graph Transformer (HGT). WWW 2020.
+用途：异构图 Transformer；如果你后续把“设备类型/寄存器类型/功能码”都纳入异构建模，HGT 是很强的参考。
+
+Hou et al. GraphMAE: Self-Supervised Masked Graph Autoencoders. KDD/相关会议 2022.
+用途：图自监督预训练；对应 STOUTER 的图 autoencoder 预训练思想，你可以用它支撑“先学图嵌入，再用于生成”。

papers/RefPaperByMarkyan04/Topic5 Graph or heterogeneous graph priors/Modeling Relational Data with Graph Convolutional Networks/Modeling Relational Data with Graph Convolutional Networks.bib

@@ -0,0 +1,9 @@
+@misc{schlichtkrull2017modelingrelationaldatagraph,
+      title={Modeling Relational Data with Graph Convolutional Networks}, 
+      author={Michael Schlichtkrull and Thomas N. Kipf and Peter Bloem and Rianne van den Berg and Ivan Titov and Max Welling},
+      year={2017},
+      eprint={1703.06103},
+      archivePrefix={arXiv},
+      primaryClass={stat.ML},
+      url={https://arxiv.org/abs/1703.06103}, 
+}

papers/RefPaperByMarkyan04/Topic5 Graph or heterogeneous graph priors/Semi-Supervised Classification with Graph Convolutional Networks/Semi-Supervised Classification with Graph Convolutional Networks.bib

@@ -0,0 +1,9 @@
+@misc{kipf2017semisupervisedclassificationgraphconvolutional,
+      title={Semi-Supervised Classification with Graph Convolutional Networks}, 
+      author={Thomas N. Kipf and Max Welling},
+      year={2017},
+      eprint={1609.02907},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1609.02907}, 
+}

papers/RefPaperByMarkyan04/Topic6 DataSet Paper/Intro.txt

@@ -0,0 +1,9 @@
+ICS/工控相关公开数据集（用于训练/对照评估）
+
+你做 Modbus TCP 语义级生成，需要真实或半真实数据来学时空规律；即便不是纯 Modbus 报文级，这些 ICS 数据集对“过程变量/状态模式”也有价值（尤其你生成的是语义级而不是 raw bytes）。
+
+Mathur & Tippenhauer. SWaT: A water treatment testbed for security research（及其数据集论文/报告，2016 前后，后续大量引用）
+用途：经典 ICS 测试床数据集，常用于异常检测、过程建模；可用于你生成“寄存器值/控制量”的真实性评估。
+
+Ahmed et al. WADI（Water Distribution testbed dataset，相关数据说明论文/报告）
+用途：同类数据集；对“工艺周期+异常”建模有帮助。

papers/RefPaperByMarkyan04/Topic6 DataSet Paper/SWaT a water treatment testbed for research and training on ICS security/Intro.txt

@@ -0,0 +1,5 @@
+ICS/工控相关公开数据集（用于训练/对照评估）
+你做 Modbus TCP 语义级生成，需要真实或半真实数据来学时空规律；即便不是纯 Modbus 报文级，这些 ICS 数据集对“过程变量/状态模式”也有价值（尤其你生成的是语义级而不是 raw bytes）。
+
+Mathur & Tippenhauer. SWaT: A water treatment testbed for security research（及其数据集论文/报告，2016 前后，后续大量引用）
+用途：经典 ICS 测试床数据集，常用于异常检测、过程建模；可用于你生成“寄存器值/控制量”的真实性评估。

papers/RefPaperByMarkyan04/Topic6 DataSet Paper/SWaT a water treatment testbed for research and training on ICS security/SWaT a water treatment testbed for research and training on ICS security.bib

@@ -0,0 +1,10 @@
+@INPROCEEDINGS{7469060,
+  author={Mathur, Aditya P. and Tippenhauer, Nils Ole},
+  booktitle={2016 International Workshop on Cyber-physical Systems for Smart Water Networks (CySWater)}, 
+  title={SWaT: a water treatment testbed for research and training on ICS security}, 
+  year={2016},
+  volume={},
+  number={},
+  pages={31-36},
+  keywords={Sensors;Actuators;Feeds;Process control;Chemicals;Chemical sensors;Security;Cyber Physical Systems;Industrial Control Systems;Cyber Attacks;Cyber Defense;Water Testbed},
+  doi={10.1109/CySWater.2016.7469060}}

papers/RefPaperByMarkyan04/Topic6 DataSet Paper/WADI a water distribution testbed for research in the design of secure cyber physical systems/WADI a water distribution testbed for research in the design of sec.bib

@@ -0,0 +1,17 @@
+@inproceedings{10.1145/3055366.3055375,
+author = {Ahmed, Chuadhry Mujeeb and Palleti, Venkata Reddy and Mathur, Aditya P.},
+title = {WADI: a water distribution testbed for research in the design of secure cyber physical systems},
+year = {2017},
+isbn = {9781450349758},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3055366.3055375},
+doi = {10.1145/3055366.3055375},
+abstract = {The architecture of a water distribution testbed (WADI), and on-going research in the design of secure water distribution system is presented. WADI consists of three stages controlled by Programmable Logic Controllers (PLCs) and two stages controlled via Remote Terminal Units (RTUs). Each PLC and RTU uses sensors to estimate the system state and the actuators to effect control. WADI is currently used to (a) conduct security analysis for water distribution networks, (b) experimentally assess detection mechanisms for potential cyber and physical attacks, and (c) understand how the impact of an attack on one CPS could cascade to other connected CPSs. The cascading effects of attacks can be studied in WADI through its connection to two other testbeds, namely for water treatment and power generation and distribution.},
+booktitle = {Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks},
+pages = {25–28},
+numpages = {4},
+keywords = {attack detection, cyber physical systems, cyber security, industrial control systems, water distribution testbed},
+location = {Pittsburgh, Pennsylvania},
+series = {CySWATER '17}
+}

papers/paper_list.txt

@@ -1,3 +0,0 @@
-Practical GAN-based synthetic IP header trace generation using NetShare
-	use GAN or Diffusion to see outcome
-