## 4 Results and Findings
### Top Ten Occurrence Keywords in Cybersecurity and ML/DL Literature  
Table 7, highlighting the top ten highest-appearing keywords in the cybersecurity and machine learning/deep learning (ML/DL) literature, offers a quick overview of the topics of interest in this field. \"Occurrences\" stands for how often each keyword appeared in the literature, \"Links\" for how many edges between these keywords we had in the network, and \"Total Link Strength\" for the strength of connections between these keywords. The most common word, by far, was the keyword artificial intelligence (AI), which surfaced 476 times, with a maximum link strength of 5853. It is not that surprising, though. AI is fundamental in many cybersecurity applications, mainly anomaly detection, behaviour analysis, and automation [51]. Due to the high frequency and strong link strength, research on this subject relies heavily on AI, as multiple studies strongly agree with using AI-based models and algorithms. The keyword \"Classifier\" is next to AI, as it was linked with AI 472 times in total, with a link strength of 6026. This implies that classification algorithms play a crucial part in cybersecurity where their task of identifying malicious activities (e.g., malware, intrusion) is needed. This idea is reinforced by the fact that the strength of the connection with AI also makes us realise that ML methods, like supervised learning techniques, are significant in cybersecurity. The third most frequent keyword is 'Intrusion Detection System' (IDS), which has 422 uses and 5499 link strength. The detection of unauthorised access or network threats is a core area of cybersecurity, and IDS reflects the continued importance of this activity. This keyword's placement at the top emphasises the trend to build real-time, automated systems that detect and neutralise intrusions, sometimes with machine learning. Malicious software (malware), which appeared 317 times with a link strength of 3798, has become a growing concern for cybersecurity threats. Other keywords often containing the word malware (e.g., AI, classifiers, intrusion detection) suggest that malware is relevant to multiple aspects of cybersecurity research. The strength of the connection between malware and the above technologies signals a concerted approach to deploying more advanced algorithms to contend with it. Another keyword, \"Integration\", was found 263 times, describing the rising interest in integrating various technologies and systems into cybersecurity frameworks. Given the criticality of integration for next-generation security systems involving sophisticated machine learning models, diverse detection techniques, and response strategies, it is crucial to address the complexities created by heterogeneity in accepting, handling, and managing data [18]. With its focus on integration, this shift suggests that holistic and connected responses to cybersecurity problems are the way to move forward. The sixth position is the convolutional neural network (CNN), which has 254 observations and a link strength of 3316. CNNs are particularly suited for pattern recognition \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Keyword** & **Occurrences** & **Links** & **Total Link Strength** & **Cluster** \\ \hline
1 & Artificial Intelligence & 476 & 1088 & 5853 & 2 \\
2 & Classifier & 472 & 1088 & 6026 & 1 \\
3 & Intrusion Detection System & 422 & 1061 & 5499 & 1 \\
4 & Malware & 317 & 944 & 3798 & 3 \\
5 & Integration & 263 & 970 & 3530 & 2 \\
6 & Convolutional Neural Network & 254 & 976 & 3316 & 3 \\
7 & Feature Selection & 248 & 892 & 3391 & 1 \\
8 & Decision Tree & 210 & 869 & 3005 & 1 \\
9 & Support Vector Machine & 202 & 866 & 2862 & 1 \\
10 & Physical System & 160 & 722 & 1782 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Top 10 keyword occurrence in cybersecurity and ML/DL literature. tasks in cybersecurity, such as image analysis in malware detection or network traffic monitoring.