## 4 Results
### Analysis Results of LDA Topic Modeling  
#### 4.4.1 Coherence Score Measurement Results  
The optimal number of topics for LDA modeling was determined based on the methodology of [PERSON] et al. [32], with the parameters set as follows: \(\alpha\) = 0.1, \(\beta\) = 0.01, and 1000 iterations. The number of topics was gradually increased from two to fifteen to evaluate the coherence scores, a critical metric for assessing the interpretability of the topics generated  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Word** & **Degree Centrality** & **Word** & **Closeness Centrality** & **Word** & **Betweenness Centrality** \\ \hline
1 & ChatGPT & 0.93878 & model & 0.67606 & ChatGPT & 0.02865 \\
2 & AI & 0.87755 & language & 0.5931 & AI & 0.02539 \\
3 & model & 0.81633 & LLM & 0.28357 & result & 0.02225 \\
4 & result & 0.81633 & ChatGPT & 0.25657 & datum & 0.01983 \\
5 & datum & 0.79592 & AI & 0.1293 & model & 0.01907 \\
6 & LLM & 0.77551 & use & 0.07502 & technology & 0.01786 \\
7 & technology & 0.77551 & tool & 0.06478 & LLM & 0.01683 \\
8 & analysis & 0.7551 & application & 0.05542 & analysis & 0.01675 \\
9 & development & 0.71429 & technology & 0.0525 & development & 0.015 \\
10 & application & 0.69388 & learning & 0.03999 & challenge & 0.01393 \\
11 & tool & 0.67347 & intelligence & 0.03711 & application & 0.01301 \\
12 & education & 0.67347 & capability & 0.035 & student & 0.01177 \\
13 & security & 0.67347 & performance & 0.03384 & education & 0.01171 \\
14 & challenge & 0.67347 & education & 0.02849 & security & 0.01165 \\
15 & capability & 0.63265 & impact & 0.02775 & capability & 0.01117 \\
16 & student & 0.63265 & generation & 0.0238 & user & 0.01016 \\
17 & system & 0.63265 & response & 0.02245 & tool & 0.01007 \\
18 & performance & 0.61224 & student & 0.01998 & information & 0.00916 \\
19 & field & 0.61224 & datum & 0.0199 & response & 0.00914 \\
20 & method & 0.61224 & system & 0.01972 & performance & 0.00883 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Keyword centrality metrics in GAI security research.  
by the model. The six topics with the highest coherence score of 0.523 were identified as the most meaningful and interpretable, as shown in Figure 7.  
Once the optimal number of topics was established, the top ten most frequent keywords for each of the six topics were identified, and they are summarized in Table 3. To ensure the accuracy and representativeness of the topic labeling, a thorough review of the keywords and relevant abstracts from the research bibliographies was conducted. This review involved collaboration with experts, including researchers with over five years of experience in IT institutions and professors specializing in text mining. Properly naming the topics was crucial for providing clear and concise representations of the central themes emerging from the LDA model. Through a meticulous examination of the keywords and a comprehensive review of the context provided by the abstracts, the most appropriate topic names were assigned, ensuring that they accurately reflected the underlying research areas related to GAI and security.  
The analysis revealed six distinct topics within the research on GAI and its security applications, as illustrated in Figure 8. Among these, the topic \"AI in Educational Security\" emerged as the most prominent, representing 40% of the papers analyzed. This suggests a significant focus on the integration of AI in educational environments, particularly with respect to security concerns. In contrast, the topic \"Healthcare Security with AI\" was the least represented, comprising only 5% of the topics. This reflects a specialized but growing area of research within the broader field of GAI and security. The distribution of topics highlights the diverse applications of GAI, with a strong emphasis on its role in education, while acknowledging the increasing importance of security in healthcare, albeit on a smaller scale.