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268 lines
13 KiB
Plaintext
\begin{thebibliography}{10}
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\providecommand{\url}[1]{\texttt{#1}}
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\providecommand{\urlprefix}{URL }
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\providecommand{\doi}[1]{https://doi.org/#1}
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\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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{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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\bibitem{kong2021diffwaveversatilediffusionmodel}
|
||
Kong, Z., Ping, W., Huang, J., Zhao, K., Catanzaro, B.: Diffwave: A versatile
|
||
diffusion model for audio synthesis (2021)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\bibitem{lin1991divergence}
|
||
Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on
|
||
Information Theory \textbf{37}(1), 145--151 (1991)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\bibitem{nist2023sp80082}
|
||
{National Institute of Standards and Technology}: Guide to operational
|
||
technology (ot) security. Special Publication 800-82 Rev. 3, NIST (sep 2023)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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), 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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\bibitem{shin}
|
||
Shin, H.K., Lee, W., Choi, S., Yun, J.H., Min, B.G., Kim, H.: Hai security
|
||
dataset (2023)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\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)
|
||
|
||
\end{thebibliography}
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