## 5 Conclusions and Future Research  
In this work, we initially transcribed the thematic contents inherent to system logs using a numerical coding scheme that assigns unique identifiers to each topic, modeling thematic specificity and its contextual interconnections. Subsequently, we performed an extraction and analytical synthesis operation on the thematic vectors corresponding to the entries in an HDFS system log, allowing the obtainment of a much-condensed log, reducing the number of lines, tokens, and thematic codes by 99%, and maintaining semantic similarity. The flexibility of the methodological approach suggests a cross-sectional adaptability for its application in HDFS log files and other computer system architectures. The expansion of this prototype to a thematic plurality could be carried out by integrating Wordnet World instead of WordNet, which would expand the lexical index's linguistic spectrum to multiple languages. A comparative study of the pre- and post-processing datasets was conducted using hierarchical thematic classification models. The intrinsic similarity preservation of the generated embeddings was evidenced, demonstrating a high cosine similarity coefficient (0.9724), thus corroborating the retention of a shared core of meaning and semantic context between the two data instances. This methodology was subjected to an empirical analysis of its effectiveness and efficiency using a 'bert-base-uncased' model. The quantitative evaluation focused on traditional precision, recall, and F1-score metrics, demonstrating that the application of this compaction technique not only maintains semantic similarity but also allows anomaly detection similar to other methods used in the literature. In conclusion, the implementation of hierarchical thematic classification in system log collection systems is revealed as an efficient vector for data compression without compromising semantic integrity. This compression proves to be a key component in monitoring infrastructure, optimizing both alert management in Security Information and Event Management (SIEM) systems and anomaly detection through deep learning language models (LLMs), mitigating operational overload and alert fatigue. Conceptualization, V.C. and C.C. ; methodology, V.C. ; software, V.C. and C.C. ; validation, V.C. ; formal analysis, V.C. and C.C. ; investigation, V.C. and C.C. ; resources, V.C. and C.C. ; data curation, V.C. and C.C. ; writing--original draft preparation, V.C. and C.C. ; writing--review and editing, F.C., V.C. and F.C. ; visualization, V.C. and F.C. ; supervision, V.C. and F.C. ; project administration, V.C. and F.C. ; funding acquisition, F.C. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline PCA & 1.00 & 0.67 & 0.70 \\ IM & 1.00 & 0.83 & 0.80 \\ LogCluster & 1.00 & 0.96 & 0.96 \\ SVM & 0.62 & 0.73 & 0.73 \\ DeepLog & 1.00 & 0.78 & 0.98 \\ LogRobust & 0.81 & 0.91 & 0.91 \\ BERT\_20M & 0.88 & 0.86 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of HDFS_20M_HC log. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare no conflicts of interest. References  
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages. _Symmetry_**2021**, _13_, 826. [CrossRef]
* _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response. _Appl. Sci._**2023**, _13_, 6610. [CrossRef]
* [PERSON] et al.