## 3 Background
### Literature Review Methodology  
In order to carry out an exhaustive and rigorous bibliographic review, a methodology was implemented that combines the use of specialized databases, such as WoS and Scopus, with an automated process of filtering and consolidation of articles. This approach made it possible to extract recent studies, no more than 5 years old, that are relevant and pertinent to the central themes of the research work. In addition, a Python system was developed and implemented to facilitate the management and elimination of duplicates, in order to optimize the time and resources invested in the review. A total of seven queries were carried out in two of the main scientific databases: WoS and Scopus. The queries were designed to cover different key aspects of this work, as detailed in Table 1. Each query was run on both platforms, and the results were exported in BibTeX format (extension.bib), with as much metadata as possible, according to the availability in each database. The exported files from each search were stored for further processing, using the Python (version 3.10) script developed to manage bibliographic references. This script will be in charge of filtering and consolidating the articles, eliminating duplicates, and applying additional relevance criteria, which will be detailed below. The article filtering process begins with the initial consolidation of the results into a single dataset and the elimination of duplicate entries. To accomplish this task, the bibtexparser and pandas libraries were used, which allowed for efficient loading and manipulation of.bib files. Then, a process of discarding articles with key information, such as title, author, year of publication, abstract, and DOI, was applied. This last field was used to identify and eliminate duplicates, since it is a unique identifier for each article, and after the filtering process, a total of 298 articles were obtained, which were consolidated in a single file and considered for subsequent review. At a second level of filtering, criteria were applied to determine the relevance of the articles for the specific objectives of the study. For this \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Research Query** & **WoS** & **Scopus** & **Total** \\ \hline DDoS AND Cloud AND (Machine learning OR & 174 & 43 & 217 \\ deep learning OR artificial intelligence) & & & \\ DDoS AND Cloud AND (Data analysis OR & 68 & 83 & 151 \\ Predictive Algorithm) & & & \\ DDoS AND Cloud AND Analysis Behaviour & 11 & 37 & 48 \\ DDoS AND Cloud AND (Hybrid OR Metaheuristic & 56 & 138 & 194 \\ OR Metaheuristics) & & & \\ DDoS AND Cloud AND (Machine learning & 2 & 1 & 3 \\ OR deep learning) AND (Metaheuristic OR & & & \\ Metaheuristics) & & & \\ DDoS AND Cloud AND (Predictive algorithm) & 2 & 9 & 11 \\ DDoS AND Cloud AND (Metaheuristic OR & 4 & 6 & 10 \\ Metaheuristics) & & & \\ \hline Total & 317 & 317 & 634 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of results per search in WoS and Scopus (without removing duplicates). purpose, visualization and analysis tools were used to examine each of the articles and their metadata in order to identify the following:  
* Problem: The specific problem addressed by the article. * Methodology / Data: Methods and datasets utilized in the experimental setup. * Architecture: Relevance of the proposed or utilized architecture to IoT/Cloud environments. * ML and/or DL algorithms: Specific ML and/or DL algorithms used. * Metaheuristics: Utilization and type of metaheuristics for process optimization. * Results: Key outcomes and findings of the study. Based on this structured analysis, the articles were categorized according to the main research topics. This classification facilitated the establishment of the state-of-the-art and the selection of the most relevant references to support the proposed research, as explained in the following sections. As mentioned above, we will focus on the detailed analysis of the key issues identified in the work, which will be addressed in the following sections.