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\begin{abstract} \begin{abstract}
Industrial control systems (ICS) security research is increasingly constrained by the scarcity and limited shareability of realistic communication traces and process measurements, especially for attack scenarios. To mitigate this bottleneck, we study synthetic generation at the protocol-feature and process-signal level, where samples must simultaneously preserve temporal coherence, match continuous marginal distributions, and keep discrete supervisory variables strictly within valid vocabularies. We propose Mask-DDPM, a hybrid framework tailored to mixed-type, multi-scale ICS sequences. Mask-DDPM factorizes generation into (i) a causal Transformer trend module that rolls out a stable long-range temporal scaffold for continuous channels, (ii) a trend-conditioned residual DDPM that refines local stochastic structure and heavy-tailed fluctuations without degrading global dynamics, (iii) a masked (absorbing) diffusion branch for discrete variables that guarantees valid symbol generation by construction, and (iv) a type-aware decomposition/routing layer that aligns modeling mechanisms with heterogeneous ICS variable origins and enforces deterministic reconstruction where appropriate. Evaluated on fixed-length windows ($L=96$) derived from the HAI Security Dataset, Mask-DDPM achieves stable fidelity across seeds with mean KS = 0.3311 $\pm$ 0.0079 (continuous), mean JSD = 0.0284 $\pm$ 0.0073 (discrete), and mean absolute lag-1 autocorrelation difference = 0.2684 $\pm$ 0.0027, indicating faithful marginals, preserved short-horizon dynamics, and valid discrete semantics. The resulting generator provides a reproducible basis for data augmentation, benchmarking, and downstream ICS protocol reconstruction workflows. Industrial control systems (ICS) security research is increasingly constrained by the scarcity and limited shareability of realistic communication traces and process measurements, especially for attack scenarios. To mitigate this bottleneck, we study synthetic generation at the protocol-feature and process-signal level, where samples must simultaneously preserve temporal coherence, match continuous marginal distributions, and keep discrete supervisory variables strictly within valid vocabularies. We propose Mask-DDPM, a hybrid framework tailored to mixed-type, multi-scale ICS sequences. Mask-DDPM factorizes generation into (i) a causal Transformer trend module that rolls out a stable long-range temporal scaffold for continuous channels, (ii) a trend-conditioned residual DDPM that refines local stochastic structure and heavy-tailed fluctuations without degrading global dynamics, (iii) a masked (absorbing) diffusion branch for discrete variables that guarantees valid symbol generation by construction, and (iv) a type-aware decomposition/routing layer that aligns modeling mechanisms with heterogeneous ICS variable origins and enforces deterministic reconstruction where appropriate. Evaluated on fixed-length windows ($L=96$) derived from the HAI Security Dataset, Mask-DDPM achieves stable fidelity across seeds with mean KS = 0.3311 $\pm$ 0.0079 (continuous), mean JSD = 0.0284 $\pm$ 0.0073 (discrete), and mean absolute lag-1 autocorrelation difference = 0.2684 $\pm$ 0.0027, indicating faithful marginals, preserved short-horizon dynamics, and valid discrete semantics. The resulting generator provides a reproducible basis for data augmentation, benchmarking, and downstream ICS protocol reconstruction workflows.
\keywords{Machine Learning \and Cyber Defense \and ICS} \keywords{Machine Learning \and Attack Synthesis \and ICS}
\end{abstract} \end{abstract}
\section{Introduction} \section{Introduction}

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