## 3 Materials and Methods
### Research Data Collection and Refinement
#### 3.1.2 Refining Research Data  
Before analysis, the collected dataset underwent several preprocessing steps to enhance data quality and ensure the accuracy of the analysis. Since keyword data in academic articles are often entered inconsistently--due to variations in terminology, formatting, and use of subscripts--direct use of such data could introduce errors [32]. To mitigate these issues, the keyword data were carefully refined. The SCOPUS \"analyse search results\" function was then employed to assess the number of publications from key countries and research institutions in the field of GAI and security. This evaluation aimed to identify research trends and highlight prominent national and international institutions focused on this area. To systematically retrieve the relevant literature, the dataset was collected using a structured search query applied to the SCOPUS database. The exact query used was as follows: (\"generative AI\" OR \"ChatGPT\" OR \"GPT-3\" OR \"large language model\" OR \"LLM\") AND (\"security\" OR \"cybersecurity\" OR \"privacy\" OR \"threat detection\" OR \"adversarial attack\"). This formulation was designed to encompass a wide spectrum of research on generative AI and security-related topics, ensuring broad coverage whilemaintaining relevance to the study. The search period was set from June 2022 to June 2024, reflecting the rapid advancements in generative AI security research. Although GPT-4, GPT-4o, and DeepSeek are notable advancements in LLM development, they were not explicitly included in the search terms due to several reasons, including publication timeline constraints: GPT-4 was released in March 2023, while GPT-4o and DeepSeek were introduced in May 2024. Given the time required for peer review and indexing, the presence of peer-reviewed articles on these models in SCOPUS at the time of data collection was limited. Limited representation in SCOPUS-indexed literature: While these models have been extensively discussed in industry reports and preprint repositories (e.g., arXiv), their inclusion in peer-reviewed SCOPUS-indexed journals and conferences remains low. Generalized search strategy: Instead of focusing on specific model names, the search query used broader terms such as \"LLM\" and \"large language model\", ensuring that relevant studies on newer models were included if indexed under general terminology.  
Following the retrieval process, the dataset underwent several critical preprocessing steps to enhance data consistency and reliability:  
* Case unification: All keywords were converted to lowercase to eliminate duplicates caused by case variations. For example, \"ChatGPT\" and \"chatgpt\" were standardized to a single keyword.
* Removal of special characters: Special characters such as hyphens (-), slashes (/), and spaces were removed to unify keywords with the same meaning. For example, \"AI-based\" and \"AI based\" were combined into a single term.
* Elimination of non-essential terms: Irrelevant terms that did not contribute to the analysis objectives were removed to retain only keywords with significant analytical value.
* Consolidating synonymous terms: Terms with similar meanings were standardized to ensure consistency across the dataset.  
These refinements were crucial to ensuring the data's consistency and reliability, which, in turn, enhanced the credibility of the analysis results. Table 1 provides examples of the refined keywords used in the final analysis. NetMiner 4, a Python-based analysis tool, was utilized for the analysis. The analysis included keyword frequency-inverse document frequency (TF-IDF) analysis, keyword centrality analysis, and LDA topic modeling [34]. Before topic modeling, the coherence score (\(C_{v}\) value) was measured, and the six highest-scoring topics were selected. Representative papers with high weighting for each topic were identified, providing a comprehensive explanation of topic selection and detailed discussions on their significance.