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PDF statistics: - 311 PDF objects out of 1000 (max. 8388607) + 313 PDF objects out of 1000 (max. 8388607) 0 named destinations out of 1000 (max. 500000) - 13845 words of extra memory for PDF output out of 14400 (max. 10000000) + 13851 words of extra memory for PDF output out of 14400 (max. 10000000) diff --git a/LaTeX2e+Proceedings+Templates+download/main.pdf b/LaTeX2e+Proceedings+Templates+download/main.pdf index 33d6e8d..da046c3 100644 Binary files a/LaTeX2e+Proceedings+Templates+download/main.pdf and b/LaTeX2e+Proceedings+Templates+download/main.pdf differ diff --git a/LaTeX2e+Proceedings+Templates+download/main.tex b/LaTeX2e+Proceedings+Templates+download/main.tex index 7ac998f..644385d 100644 --- a/LaTeX2e+Proceedings+Templates+download/main.tex +++ b/LaTeX2e+Proceedings+Templates+download/main.tex @@ -10,8 +10,17 @@ \usepackage{booktabs} \usepackage[expansion=false]{microtype} \usepackage{float} +\usepackage{enumitem} \usepackage{url} +% Compact internal spacing only; page layout/margins remain unchanged. +\setlength{\textfloatsep}{10pt plus 2pt minus 2pt} +\setlength{\floatsep}{8pt plus 2pt minus 2pt} +\setlength{\intextsep}{10pt plus 2pt minus 2pt} +\setlength{\abovecaptionskip}{4pt} +\setlength{\belowcaptionskip}{0pt} +\setlist{topsep=3pt,itemsep=2pt,parsep=0pt,partopsep=0pt} + % Compatibility shim: the source manuscript uses natbib-style citep commands. \newcommand{\citep}[1]{\cite{#1}} @@ -28,6 +37,7 @@ School of Artificial Intelligence, South China Normal University, Guangzhou, Gua \email{huan.yang@m.scnu.edu.cn}} \begin{document} +\raggedbottom \maketitle \begin{abstract} @@ -68,7 +78,7 @@ A key empirical and methodological tension in ICS synthesis is that temporal rea \begin{figure}[htbp] \centering - \includegraphics[width=0.8\textwidth]{fig-design-v4-from-user-svg-cropped.pdf} + \includegraphics[width=0.995\textwidth,trim=4pt 4pt 4pt 4pt,clip]{fig-design-v4-from-user-svg-cropped.pdf} \caption{Masked-DDPM: Unified Synthesis for ICS traffic} \label{fig:design} \end{figure} @@ -135,7 +145,7 @@ We train the denoiser using the standard DDPM $\epsilon$-prediction objective: \mathcal{L}_{\text{cont}}(\theta) = \mathbb{E}_{k,\bm{r}_0,\boldsymbol{\epsilon}} \left[ \bigl\| \boldsymbol{\epsilon} - \boldsymbol{\epsilon}_{\theta}(\bm{r}_k, k, \hat{\bm{S}}) \bigr\|_2^2 \right]. \label{eq:ddpm_loss} \end{equation} -Because diffusion optimization can exhibit timestep imbalance (i.e., some timesteps dominate gradients), we optionally apply an SNR-based reweighting consistent with Min-SNR training: +Because diffusion optimization can exhibit time-step imbalance (i.e., some steps dominate gradients), we optionally apply an SNR-based reweighting consistent with Min-SNR training: \begin{equation} \mathcal{L}^{\text{snr}}_{\text{cont}}(\theta) = \mathbb{E}_{k,\bm{r}_0,\boldsymbol{\epsilon}} \left[ w_k \bigl\| \boldsymbol{\epsilon} - \boldsymbol{\epsilon}_{\theta}(\bm{r}_k, k, \hat{\bm{S}}) \bigr\|_2^2 \right], \label{eq:snr_loss} @@ -317,9 +327,9 @@ Taken together, the benchmark supports a focused claim. Mask-DDPM already provid % 5. Conclusion and Future Work \section{Conclusion and Future Work} \label{sec:conclusion} -This paper addresses the data scarcity and shareability barriers that limit machine-learning research for industrial control systems (ICS) security by proposing Mask-DDPM, a hybrid synthetic telemetry generator at the protocol-feature level. By combining a causal Transformer trend module, a trend-conditioned residual DDPM, a masked diffusion branch for discrete variables, and a type-aware routing layer, the framework preserves long-horizon temporal structure, improves local distributional fidelity, and guarantees discrete semantic legality. On windows derived from the HAI Security Dataset, the model achieves stable mixed-type fidelity across seeds, with mean KS = 0.3311 $\pm$ 0.0079 on continuous features, mean JSD = 0.0284 $\pm$ 0.0073 on discrete features, and mean absolute lag-1 autocorrelation difference = 0.2684 $\pm$ 0.0027. +This paper addresses data scarcity and sharing barriers in industrial control systems (ICS) security by proposing Mask-DDPM, a hybrid synthetic telemetry generator at the protocol-feature level. By combining a causal Transformer trend module, a trend-conditioned residual DDPM, a masked diffusion branch for discrete variables, and a type-aware routing layer, the framework preserves long-horizon temporal structure, improves local distributional fidelity, and guarantees discrete semantic legality. On windows derived from the HAI Security Dataset, the model achieves stable mixed-type fidelity across seeds, with mean KS = 0.3311 $\pm$ 0.0079 on continuous features, mean JSD = 0.0284 $\pm$ 0.0073 on discrete features, and mean absolute lag-1 autocorrelation difference = 0.2684 $\pm$ 0.0027. -Overall, Mask-DDPM provides a reproducible foundation for generating shareable and semantically valid ICS feature sequences for data augmentation, benchmarking, and downstream packet/trace reconstruction workflows. Future work will proceed in two complementary directions. Vertically, we will strengthen the theoretical foundation of the framework by introducing more explicit control-theoretic constraints, structured state-space or causal priors, and formal transition models for supervisory logic, so that legality, stability, and cross-channel coupling can be characterized more rigorously. Horizontally, we will extend the framework beyond the current setting to additional industrial control protocols such as Modbus/TCP, DNP3, IEC 104, and OPC UA, and investigate analogous adaptations to automotive communication protocols such as CAN/CAN FD and automotive Ethernet. A related extension is controllable attack or violation injection on top of legal base traces, enabling reproducible adversarial benchmarks for anomaly detection and intrusion-detection studies. +Overall, Mask-DDPM provides a reproducible foundation for shareable, semantically valid ICS feature sequences for data augmentation, benchmarking, and downstream packet/trace reconstruction workflows. Future work will proceed in two complementary directions. Vertically, we will strengthen the theoretical foundation of the framework by introducing more explicit control-theoretic constraints, structured state-space or causal priors, and formal transition models for supervisory logic, so that legality, stability, and cross-channel coupling can be characterized more rigorously. Horizontally, we will extend the framework beyond the current setting to additional industrial control protocols such as Modbus/TCP, DNP3, IEC 104, and OPC UA, and investigate analogous adaptations to automotive communication protocols such as CAN/CAN FD and automotive Ethernet. A related extension is controllable attack or violation injection on top of legal base traces, enabling reproducible adversarial benchmarks for anomaly detection and intrusion-detection studies. \bibliographystyle{splncs04} \bibliography{references}