\begin{thebibliography}{10}
\providecommand{\url}[1]{\texttt{#1}}
\providecommand{\urlprefix}{URL }
\providecommand{\doi}[1]{https://doi.org/#1}

\bibitem{10.1145/3055366.3055375}
Ahmed, C.M., Palleti, V.R., Mathur, A.P.: Wadi: a water distribution testbed
  for research in the design of secure cyber physical systems. In: Proceedings
  of the 3rd International Workshop on Cyber-Physical Systems for Smart Water
  Networks. p. 25–28. CySWATER '17, Association for Computing Machinery, New
  York, NY, USA (2017). \doi{10.1145/3055366.3055375},
  \url{https://doi.org/10.1145/3055366.3055375}

\bibitem{info16100910}
Ali, J., Ali, S., Al~Balushi, T., Nadir, Z.: Intrusion detection in industrial
  control systems using transfer learning guided by reinforcement learning.
  Information  \textbf{16}(10) (2025). \doi{10.3390/info16100910},
  \url{https://www.mdpi.com/2078-2489/16/10/910}

\bibitem{austin2021structured}
Austin, J., Johnson, D.D., Ho, J., Tarlow, D., van~den Berg, R.: Structured
  denoising diffusion models in discrete state-spaces. In: Ranzato, M.,
  Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in
  Neural Information Processing Systems. vol.~34, pp. 17981--17993. Curran
  Associates, Inc. (2021),
  \url{https://proceedings.neurips.cc/paper_files/paper/2021/file/958c530554f78bcd8e97125b70e6973d-Paper.pdf}

\bibitem{coletta2023constrained}
Coletta, A., Gopalakrishnan, S., Borrajo, D., Vyetrenko, S.: On the constrained
  time-series generation problem. In: Oh, A., Naumann, T., Globerson, A.,
  Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information
  Processing Systems. vol.~36, pp. 61048--61059. Curran Associates, Inc.
  (2023),
  \url{https://proceedings.neurips.cc/paper_files/paper/2023/file/bfb6a69c0d9e2bc596e1cd31f16fcdde-Paper-Conference.pdf}

\bibitem{dai2019transformerxlattentivelanguagemodels}
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., Salakhutdinov, R.:
  Transformer-{XL}: Attentive language models beyond a fixed-length context.
  In: Korhonen, A., Traum, D., M{\`a}rquez, L. (eds.) Proceedings of the 57th
  Annual Meeting of the Association for Computational Linguistics. pp.
  2978--2988. Association for Computational Linguistics, Florence, Italy (Jul
  2019). \doi{10.18653/v1/P19-1285}, \url{https://aclanthology.org/P19-1285/}

\bibitem{godefroid2017learnfuzzmachinelearninginput}
Godefroid, P., Peleg, H., Singh, R.: Learn\&fuzz: Machine learning for input
  fuzzing. In: 2017 32nd IEEE/ACM International Conference on Automated
  Software Engineering (ASE). pp. 50--59 (2017). \doi{10.1109/ASE.2017.8115618}

\bibitem{hang2023efficient}
Hang, T., Gu, S., Li, C., Bao, J., Chen, D., Hu, H., Geng, X., Guo, B.:
  Efficient diffusion training via min-snr weighting strategy. In: Proceedings
  of the IEEE/CVF International Conference on Computer Vision (ICCV). pp.
  7441--7451 (October 2023)

\bibitem{NEURIPS2020_4c5bcfec}
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In:
  Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances
  in Neural Information Processing Systems. vol.~33, pp. 6840--6851. Curran
  Associates, Inc. (2020),
  \url{https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf}

\bibitem{ho2020denoising}
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In:
  Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances
  in Neural Information Processing Systems. vol.~33, pp. 6840--6851. Curran
  Associates, Inc. (2020),
  \url{https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf}

\bibitem{hoogeboom2021argmaxflowsmultinomialdiffusion}
Hoogeboom, E., Nielsen, D., Jaini, P., Forr\'{e}, P., Welling, M.: Argmax flows
  and multinomial diffusion: Learning categorical distributions. In: Ranzato,
  M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in
  Neural Information Processing Systems. vol.~34, pp. 12454--12465. Curran
  Associates, Inc. (2021),
  \url{https://proceedings.neurips.cc/paper_files/paper/2021/file/67d96d458abdef21792e6d8e590244e7-Paper.pdf}

\bibitem{jiang2023netdiffusionnetworkdataaugmentation}
Jiang, X., Liu, S., Gember-Jacobson, A., Bhagoji, A.N., Schmitt, P., Bronzino,
  F., Feamster, N.: Netdiffusion: Network data augmentation through
  protocol-constrained traffic generation. Proc. ACM Meas. Anal. Comput. Syst.
  \textbf{8}(1) (Feb 2024). \doi{10.1145/3639037},
  \url{https://doi.org/10.1145/3639037}

\bibitem{10.1007/s10844-022-00753-1}
Koay, A.M., Ko, R.K.L., Hettema, H., Radke, K.: Machine learning in industrial
  control system (ics) security: current landscape, opportunities and
  challenges. Journal of Intelligent Information Systems  \textbf{60}(2),
  377--405 (2023)

\bibitem{kollovieh2023tsdiff}
Kollovieh, M., Ansari, A.F., Bohlke-Schneider, M., Zschiegner, J., Wang, H.,
  Wang, Y.B.: Predict, refine, synthesize: Self-guiding diffusion models for
  probabilistic time series forecasting. In: Oh, A., Naumann, T., Globerson,
  A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information
  Processing Systems. vol.~36, pp. 28341--28364. Curran Associates, Inc.
  (2023),
  \url{https://proceedings.neurips.cc/paper_files/paper/2023/file/5a1a10c2c2c9b9af1514687bc24b8f3d-Paper-Conference.pdf}

\bibitem{kong2021diffwaveversatilediffusionmodel}
Kong, Z., Ping, W., Huang, J., Zhao, K., Catanzaro, B.: Diffwave: A versatile
  diffusion model for audio synthesis (2021),
  \url{https://arxiv.org/abs/2009.09761}

\bibitem{pmlr-v202-kotelnikov23a}
Kotelnikov, A., Baranchuk, D., Rubachev, I., Babenko, A.: {T}ab{DDPM}:
  Modelling tabular data with diffusion models. In: Krause, A., Brunskill, E.,
  Cho, K., Engelhardt, B., Sabato, S., Scarlett, J. (eds.) Proceedings of the
  40th International Conference on Machine Learning. Proceedings of Machine
  Learning Research, vol.~202, pp. 17564--17579. PMLR (23--29 Jul 2023),
  \url{https://proceedings.mlr.press/v202/kotelnikov23a.html}

\bibitem{li2022diffusionlmimprovescontrollabletext}
Li, X., Thickstun, J., Gulrajani, I., Liang, P.S., Hashimoto, T.B.:
  Diffusion-lm improves controllable text generation. In: Koyejo, S., Mohamed,
  S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural
  Information Processing Systems. vol.~35, pp. 4328--4343. Curran Associates,
  Inc. (2022),
  \url{https://proceedings.neurips.cc/paper_files/paper/2022/file/1be5bc25d50895ee656b8c2d9eb89d6a-Paper-Conference.pdf}

\bibitem{lin1991divergence}
Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on
  Information Theory  \textbf{37}(1),  145--151 (1991). \doi{10.1109/18.61115}

\bibitem{Lin_2020}
Lin, Z., Jain, A., Wang, C., Fanti, G., Sekar, V.: Using gans for sharing
  networked time series data: Challenges, initial promise, and open questions.
  In: Proceedings of the ACM Internet Measurement Conference. p. 464–483. IMC
  '20, Association for Computing Machinery, New York, NY, USA (2020).
  \doi{10.1145/3419394.3423643}, \url{https://doi.org/10.1145/3419394.3423643}

\bibitem{liu2023pristiconditionaldiffusionframework}
Liu, M., Huang, H., Feng, H., Sun, L., Du, B., Fu, Y.: Pristi: A conditional
  diffusion framework for spatiotemporal imputation. In: 2023 IEEE 39th
  International Conference on Data Engineering (ICDE). pp. 1927--1939 (2023).
  \doi{10.1109/ICDE55515.2023.00150}

\bibitem{11087622}
Liu, X., Xu, X., Liu, Z., Li, Z., Wu, K.: Spatio-temporal diffusion model for
  cellular traffic generation. IEEE Transactions on Mobile Computing
  \textbf{25}(1),  257--271 (2026). \doi{10.1109/TMC.2025.3591183}

\bibitem{7469060}
Mathur, A.P., Tippenhauer, N.O.: Swat: a water treatment testbed for research
  and training on ics security. In: 2016 International Workshop on
  Cyber-physical Systems for Smart Water Networks (CySWater). pp. 31--36
  (2016). \doi{10.1109/CySWater.2016.7469060}

\bibitem{meng2025aflnetyearslatercoverageguided}
Meng, R., Pham, V.T., Böhme, M., Roychoudhury, A.: Aflnet five years later: On
  coverage-guided protocol fuzzing. IEEE Transactions on Software Engineering
  \textbf{51}(4),  960--974 (2025). \doi{10.1109/TSE.2025.3535925}

\bibitem{Nankya2023-gp}
Nankya, M., Chataut, R., Akl, R.: Securing industrial control systems:
  Components, cyber threats, and machine learning-driven defense strategies.
  Sensors  \textbf{23}(21) (2023). \doi{10.3390/s23218840},
  \url{https://www.mdpi.com/1424-8220/23/21/8840}

\bibitem{nist2023sp80082}
{National Institute of Standards and Technology}: Guide to operational
  technology (ot) security. Special Publication 800-82 Rev. 3, NIST (sep 2023).
  \doi{10.6028/NIST.SP.800-82r3},
  \url{https://csrc.nist.gov/pubs/sp/800/82/r3/final}

\bibitem{nie2023patchtst}
Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64
  words: Long-term forecasting with transformers. In: International Conference
  on Learning Representations (ICLR) (2023),
  \url{https://arxiv.org/abs/2211.14730}

\bibitem{rasul2021autoregressivedenoisingdiffusionmodels}
Rasul, K., Seward, C., Schuster, I., Vollgraf, R.: Autoregressive denoising
  diffusion models for multivariate probabilistic time series forecasting. In:
  Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference
  on Machine Learning. Proceedings of Machine Learning Research, vol.~139, pp.
  8857--8868. PMLR (18--24 Jul 2021),
  \url{https://proceedings.mlr.press/v139/rasul21a.html}

\bibitem{Ring_2019}
Ring, M., Schlör, D., Landes, D., Hotho, A.: Flow-based network traffic
  generation using generative adversarial networks. Computers \& Security
  \textbf{82},  156--172 (2019).
  \doi{https://doi.org/10.1016/j.cose.2018.12.012},
  \url{https://www.sciencedirect.com/science/article/pii/S0167404818308393}

\bibitem{sha2026ddpm}
Sha, Y., Yuan, Y., Wu, Y., Zhao, H.: Ddpm fusing mamba and adaptive attention:
  An augmentation method for industrial control systems anomaly data (jan
  2026). \doi{10.2139/ssrn.6055903},
  \url{https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6055903}, sSRN
  Electronic Journal

\bibitem{she2019neuzzefficientfuzzingneural}
She, D., Pei, K., Epstein, D., Yang, J., Ray, B., Jana, S.: Neuzz: Efficient
  fuzzing with neural program smoothing. In: 2019 IEEE Symposium on Security
  and Privacy (SP). pp. 803--817 (2019). \doi{10.1109/SP.2019.00052}

\bibitem{shi2024simplified}
Shi, J., Han, K., Wang, Z., Doucet, A., Titsias, M.: Simplified and generalized
  masked diffusion for discrete data. In: Globerson, A., Mackey, L., Belgrave,
  D., Fan, A., Paquet, U., Tomczak, J., Zhang, C. (eds.) Advances in Neural
  Information Processing Systems. vol.~37, pp. 103131--103167. Curran
  Associates, Inc. (2024). \doi{10.52202/079017-3277},
  \url{https://proceedings.neurips.cc/paper_files/paper/2024/file/bad233b9849f019aead5e5cc60cef70f-Paper-Conference.pdf}

\bibitem{shi2025tabdiff}
Shi, J., Xu, M., Hua, H., Zhang, H., Ermon, S., Leskovec, J.: Tabdiff: a
  mixed-type diffusion model for tabular data generation (2025),
  \url{https://arxiv.org/abs/2410.20626}

\bibitem{shin}
Shin, H.K., Lee, W., Choi, S., Yun, J.H., Min, B.G., Kim, H.: Hai security
  dataset (2023). \doi{10.34740/kaggle/dsv/5821622},
  \url{https://www.kaggle.com/dsv/5821622}

\bibitem{sikder2023transfusion}
Sikder, M.F., Ramachandranpillai, R., Heintz, F.: Transfusion: Generating long,
  high fidelity time series using diffusion models with transformers. Machine
  Learning with Applications  \textbf{20},  100652 (2025).
  \doi{https://doi.org/10.1016/j.mlwa.2025.100652},
  \url{https://www.sciencedirect.com/science/article/pii/S2666827025000350}

\bibitem{song2021score}
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.:
  Score-based generative modeling through stochastic differential equations
  (2021), \url{https://arxiv.org/abs/2011.13456}

\bibitem{stenger2024survey}
Stenger, M., Leppich, R., Foster, I.T., Kounev, S., Bauer, A.: Evaluation is
  key: a survey on evaluation measures for synthetic time series. Journal of
  Big Data  \textbf{11}(1), ~66 (2024)

\bibitem{tashiro2021csdiconditionalscorebaseddiffusion}
Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based
  diffusion models for probabilistic time series imputation. In: Ranzato, M.,
  Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in
  Neural Information Processing Systems. vol.~34, pp. 24804--24816. Curran
  Associates, Inc. (2021),
  \url{https://proceedings.neurips.cc/paper_files/paper/2021/file/cfe8504bda37b575c70ee1a8276f3486-Paper.pdf}

\bibitem{vaswani2017attention}
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N.,
  Kaiser, L.u., Polosukhin, I.: Attention is all you need. In: Guyon, I.,
  Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S.,
  Garnett, R. (eds.) Advances in Neural Information Processing Systems.
  vol.~30. Curran Associates, Inc. (2017),
  \url{https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf}

\bibitem{10.1145/1151659.1159928}
Vishwanath, K.V., Vahdat, A.: Realistic and responsive network traffic
  generation. SIGCOMM Comput. Commun. Rev.  \textbf{36}(4),  111–122 (Aug
  2006). \doi{10.1145/1151659.1159928},
  \url{https://doi.org/10.1145/1151659.1159928}

\bibitem{wen2024diffstgprobabilisticspatiotemporalgraph}
Wen, H., Lin, Y., Xia, Y., Wan, H., Wen, Q., Zimmermann, R., Liang, Y.:
  Diffstg: Probabilistic spatio-temporal graph forecasting with denoising
  diffusion models. In: Proceedings of the 31st ACM International Conference on
  Advances in Geographic Information Systems. SIGSPATIAL '23, Association for
  Computing Machinery, New York, NY, USA (2023). \doi{10.1145/3589132.3625614},
  \url{https://doi.org/10.1145/3589132.3625614}

\bibitem{wu2022autoformerdecompositiontransformersautocorrelation}
Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: Decomposition transformers with
  auto-correlation for long-term series forecasting. In: Ranzato, M.,
  Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in
  Neural Information Processing Systems. vol.~34, pp. 22419--22430. Curran
  Associates, Inc. (2021),
  \url{https://proceedings.neurips.cc/paper_files/paper/2021/file/bcc0d400288793e8bdcd7c19a8ac0c2b-Paper.pdf}

\bibitem{yang2001interlock}
Yang, S., Tan, L., He, C.: Automatic verification of safety interlock systems
  for industrial processes. Journal of Loss Prevention in the Process
  Industries  \textbf{14}(5),  379--386 (2001).
  \doi{https://doi.org/10.1016/S0950-4230(01)00014-6},
  \url{https://www.sciencedirect.com/science/article/pii/S0950423001000146}

\bibitem{10.1145/3544216.3544251}
Yin, Y., Lin, Z., Jin, M., Fanti, G., Sekar, V.: Practical gan-based synthetic
  ip header trace generation using netshare. In: Proceedings of the ACM SIGCOMM
  2022 Conference. p. 458–472. SIGCOMM '22, Association for Computing
  Machinery, New York, NY, USA (2022). \doi{10.1145/3544216.3544251},
  \url{https://doi.org/10.1145/3544216.3544251}

\bibitem{yoon2019timegan}
Yoon, J., Jarrett, D., van~der Schaar, M.: Time-series generative adversarial
  networks. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\textquotesingle
  Alch\'{e}-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information
  Processing Systems. vol.~32. Curran Associates, Inc. (2019),
  \url{https://proceedings.neurips.cc/paper_files/paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf}

\bibitem{yuan2025ctu}
Yuan, Y., Sha, Y., Zhao, H.: Ctu-ddpm: Generating industrial control system
  time-series data with a cnn-transformer hybrid diffusion model. In:
  Proceedings of the 2025 International Symposium on Artificial Intelligence
  and Computational Social Sciences. p. 547–552. AICSS '25, Association for
  Computing Machinery, New York, NY, USA (2025). \doi{10.1145/3776759.3776845},
  \url{https://doi.org/10.1145/3776759.3776845}

\bibitem{zhou2021informerefficienttransformerlong}
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.:
  Informer: Beyond efficient transformer for long sequence time-series
  forecasting. Proceedings of the AAAI Conference on Artificial Intelligence
  \textbf{35}(12),  11106--11115 (May 2021). \doi{10.1609/aaai.v35i12.17325},
  \url{https://ojs.aaai.org/index.php/AAAI/article/view/17325}

\bibitem{zhou2022fedformerfrequencyenhanceddecomposed}
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: {FED}former: Frequency
  enhanced decomposed transformer for long-term series forecasting. In:
  Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S.
  (eds.) Proceedings of the 39th International Conference on Machine Learning.
  Proceedings of Machine Learning Research, vol.~162, pp. 27268--27286. PMLR
  (17--23 Jul 2022), \url{https://proceedings.mlr.press/v162/zhou22g.html}

\end{thebibliography}