# Anomaly Detection Method for Unknown Protocols in a Power Plant ICS Network with Decision Tree
## 5 Conclusions  
In conclusion, this paper presents a decision tree-based model for detecting illegal assets and abnormal traffic in the control network of a power plant. Our results demonstrated the effectiveness of the proposed model in detecting anomalies in the control network of power plants. We also highlighted the importance of applying machine learning to ensure system stability in unpredictable environments. Future research should focus on comparing various artificial intelligence algorithms for extracting traffic generation reference points and determining packet sizes to determine communication patterns. Additionally, it is necessary to investigate the possibility of using digital forensic techniques to check for data forgery or falsification using deep learning algorithms to increase the accuracy and detection performance of anomaly detection. Conceptualization, K.-M.L. and K.-H.L. ; methodology, K.-M.L. and M.-Y.C. ; software, J.-G.K.; validation, K.-M.L. and M.-Y.C. ; formal analysis, K.-M.L. ; investigation, M.-Y.C. ; \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Traffic** & **Flows** & **Techniques** & **Accuracy** & **Precision** & **Sensitivity** & **F1 Score** & **Fall-out** & **Duration** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{21,596} & 1.DT & 0.08 & 0.088 & 0.193 & 0.121 & 0.94 & 1.15 s \\  & & 2i-DT & 0.482 & 0.46 & 0.764 & 0.574 & 0.45 & 0.81 s \\  & & Proposed & 0.9 & 0.895 & 0.979 & 0.935 & 0.08 & 0.47 s \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{21,592} & 1.DT & 0.073 & 0.057 & 0.181 & 0.086 & 0.87 & 1.45 s \\  & & 2i-DT & 0.482 & 0.466 & 0.787 & 0.586 & 0.46 & 1.05 s \\  & & Proposed & 0.89 & 0.885 & 0.978 & 0.929 & 0.09 & 0.25 s \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{21,594} & 1.DT & 0.074 & 0.05 & 0.157 & 0.076 & 0.85 & 1.03 s \\  & & 2i-DT & 0.486 & 0.467 & 0.8 & 0.589 & 0.44 & 0.85 s \\  & & Proposed & 0.91 & 0.908 & 0.983 & 0.944 & 0.08 & 0.58 s \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{21,592} & 1.DT & 0.077 & 0.057 & 0.178 & 0.087 & 0.86 & 1.15 s \\  & & 2i-DT & 0.487 & 0.461 & 0.814 & 0.589 & 0.41 & 0.58 s \\  & & Proposed & 0.89 & 0.885 & 0.975 & 0.928 & 0.09 & 0.27 s \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{21,593} & 1.DT & 0.079 & 0.085 & 0.221 & 0.123 & 0.94 & 1.35 s \\  & & 2i-DT & 0.477 & 0.45 & 0.803 & 0.576 & 0.42 & 0.48 s \\ \cline{1-1}  & & Proposed & 0.87 & 0.856 & 0.976 & 0.915 & 0.08 & 0.15 s \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results of the traffic generation experiment. resources, K.-M.L. ; data curation, J.-G.K.; writing--original draft preparation, K.-M.L. ; writing--review and editing, K.-H.L. ; visualization, J.K; supervision, K.-H.L. ; project administration, K.-H.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. * _[PERSON] et al. (2020)_ _[PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Understanding and visualizing data iteration in machine learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 25-30 April 2020; pp. 1-13._
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