## 4 Results and Analysis
In turn, the results of subset M (records of multi services) presented a lower accuracy ranging from 79% to 81%. The accuracy results show that the best three algorithms are the random forest classifier, the simple logistics, and the decision tree classifier. Table 3 presents the precision for each algorithm for each subset. From the results obtained, it can be verified that the subsets with records by service (subsets A, B, and C) presented a precision ranging from 88% to 93%. In turn, the results of subset M (records of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & **Accuracy (\%)** & & \\ \hline
**Algorithm** & **Subset A** & **Subset B** & **Subset C** & **Subset M** & **Average** \\ \hline Random Forest & & 93.25 & 91.54 & 92.93 & 81.26 & 89.75 \\ Classifier & & 92.38 & 91.16 & 91.27 & 80.93 & 88.94 \\ \hline Decision Tree & & 91.48 & 91.22 & 92.51 & 80.56 & 88.94 \\ Classifier & & & & & \\ \hline K-Nearest Neighbors & & 91.87 & 90.91 & 92.57 & 79.27 & 88.66 \\ Support Vector & & & & & \\ Machine & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the accuracy of each algorithm for each subset. multi services) presented a precision ranging from 80% to 82%. The precision results show that the best three algorithms are the random forest classifier, the decision tree classifier, and the simple logistics. From the results presented in Tables 2 and 3, it is observed that the highest values regarding accuracy and precision were obtained for the datasets with records per service (Subset A to C). Table 4 presents the elapsed runtime for each algorithm and per subset. To obtain these results, each algorithm and subset was trained using a workstation featuring an Intel(r) CoreTM i5-9400 CPU @ 2.90 GHz, with 32 GB of RAM. From the results obtained, the decision tree classifier, the simple logistics, and the random forest classifier algorithms trained in a shorter time, in average, than the K-nearest neighbors and support vector machine algorithms. In order to test the performance of the proposed architecture, their components (the master, the classifiers, and the trainers) were implemented in Fed4 FIRE+ with Ray to distribute tasks by the available resources (other federated testbed platforms and distributed tasks tools could be used in the case that they present similar specifications). From the preliminary tests regarding models accuracy, precision, and elapsed runtime, the decision tree classifier (DTC), the random forest classifier (RFC), and the simple logistics (SLog) were selected to be deployed in the classifiers. The datasets are generated per service, as they present higher results for accuracy and precision, and they are generated in the dataset assembler, each 100,000 classified records. Five samples of the main CICDDoS2019 dataset were prepared and tested in two scenarios: \"baseline\" and \"proposed architecture\". The \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Elapsed Runtime (s)**} \\ \hline
**Algorithm** & **Subset A** & **Subset B** & **Subset C** & **Subset M** & **Average** \\ \hline Decision Tree Classifier & 2 & 5 & 16 & 4 & 6.75 \\ \hline Simple Logistics & 124 & 37 & 18 & 238 & 104.25 \\ \hline Random Forest Classifier & 161 & 782 & 1489 & 472 & 726.00 \\ \hline K-Nearest Neighbors & 426 & 6333 & 19,069 & 2988 & 7204.00 \\ \hline Support Vector Machine & 1034 & 17,919 & 47,896 & 1834 & 17,170.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Elapsed runtime results for each algorithm and per subset.