Use draw.io type taxonomy figure in methodology

arxiv-style/main.tex

@@ -1,4 +1,4 @@
-\documentclass{article}
+\documentclass{article}
 
 \usepackage{arxiv}
 
@@ -24,16 +24,16 @@
 \usepackage{caption}     % Better caption spacing
 \usepackage{float}       % Precise figure placement
 
-% 鏍囬
+% 閺嶅洭顣?
 \title{Mask-DDPM: Transformer-Conditioned Mixed-Type Diffusion for Semantically Valid ICS Telemetry Synthesis}
 
-% 鑻ヤ笉闇€瑕佹棩鏈燂紝鍙栨秷涓嬮潰涓€琛岀殑娉ㄩ噴
+% 閼汇儰绗夐棁鈧憰浣规）閺堢噦绱濋崣鏍ㄧХ娑撳娼版稉鈧悰宀€娈戝▔銊╁櫞
 \date{}
 
 \newif\ifuniqueAffiliation
 \uniqueAffiliationtrue
 
-\ifuniqueAffiliation % 鏍囧噯浣滆€呭潡
+\ifuniqueAffiliation % 閺嶅洤鍣担婊嗏偓鍛健
 \author{
     Zhenglan Chen \\
 	Aberdeen Institute of Data Science and Artificial Intelligence\\
@@ -61,10 +61,10 @@
 }
 \fi
 
-% 椤电湁璁剧疆
+% 妞ょ數婀佺拋鍓х枂
 \renewcommand{\shorttitle}{\textit{arXiv} Template}
 
-%%% PDF 鍏冩暟鎹?
+%%% PDF 閸忓啯鏆熼幑?
 \hypersetup{
 pdftitle={Your Paper Title},
 pdfsubject={cs.LG, cs.CR},
@@ -79,7 +79,7 @@ pdfkeywords={Keyword1, Keyword2, Keyword3},
 Industrial control systems (ICS) security research is increasingly constrained by the scarcity and non-shareability of realistic traffic and telemetry, especially for attack scenarios. To mitigate this bottleneck, we study synthetic generation at the protocol feature/telemetry 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-horizon 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 categorical legality 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.
 \end{abstract}
 
-% 鍏抽敭璇?
+% 閸忔娊鏁拠?
 \keywords{Machine Learning \and Cyber Defense \and ICS}
 
 % 1. Introduction
@@ -239,7 +239,7 @@ We use the following taxonomy:
 
 \begin{figure}[H]
   \centering
-  \includegraphics[width=0.98\textwidth]{typeclass-cropped.pdf}
+  \includegraphics[width=0.98\textwidth,trim=0 550 0 10,clip]{typeclass-cropped.pdf}
   \caption*{Type assignment and six-type taxonomy.}
 \end{figure}
 
@@ -411,10 +411,12 @@ Our main contributions are: (i) a causal Transformer trend module that provides
 We evaluated the approach on windows derived from the HAI Security Dataset and reported mixed-type, protocol-relevant metrics rather than a single aggregate score. Across seeds, the model achieves stable fidelity 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, indicating that Mask-DDPM preserves both marginal distributions and short-horizon dynamics while maintaining discrete legality.
 
 Overall, Mask-DDPM provides a reproducible foundation for generating shareable, semantically valid ICS feature sequences suitable for data augmentation, benchmarking, and downstream packet/trace reconstruction workflows. Building on this capability, a natural next step is to move from purely legal synthesis toward controllable scenario construction, including structured attack/violation injection under engineering constraints to support adversarial evaluation and more comprehensive security benchmarks.
-% 鍙傝€冩枃鐚?
+% 閸欏倽鈧啯鏋冮悮?
 \bibliographystyle{unsrtnat}
 \bibliography{references}
 
 \end{document}
 
 
+
+