# Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset
## 7 Result Discussion
### Quantitative Comparison  
In this section, we assess the performance of various ML classifiers by using the dataset proposed in our study. The primary objective is to determine how well each classifier performs at detecting and classifying instances within the dataset and to quantify their performance metrics. To achieve this, we employed the classifiers listed in Section 6.4.2 with their default parameters. These classifiers were selected for their ability to handle both binary and multiclass classification tasks effectively. Tables 3 and 4 present the evaluation metrics and findings obtained by using the most commonly used classifiers for classification tasks on the proposed dataset. Two approaches were utilized to test the classifiers: the \"supplied test set\" and the \"cross-validation test\". The \"supplied test set\" involves training the classifiers on one dataset (the training dataset) and then testing them on a separate dataset (the testing dataset). Specifically, 70% of the data are retained for classifier training, and the remaining 30% are used for testing to calculate the model's accuracy. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Classifier** & **TPR \%** & **FPR \%** & **Prec \%** & **Rec \%** & **F1 \%** & **MCC \%** & **ROC \%** & **PRC \%** & **DA \%** \\ \hline Naive Bayes & 0.940 & 0.051 & 0.946 & 0.940 & 0.940 & 0.886 & 0.997 & 0.994 & 93.9625 \\ \hline Decision Tree (J48) & 1.000 & 1.000 & 0.000 & 1.000 & 1.000  
On the other hand, the \"cross-validation test\" (with ten folds) creates multiple samples from the training dataset. The model is repeatedly run ten times, with one fold saved for validation and the other nine folds used for model training in each run. The average of all ten folds yields the cross-validation result. This approach trains the model on various random data selections, enhancing its reliability and robustness [76]. By employing these evaluation methods, this study aims to comprehensively assess the performance of the classifiers and determine their effectiveness at handling the proposed dataset for classification tasks. Using supplied test sets and cross-validation tests ensures a thorough evaluation of the classifiers' capabilities and provides valuable insights for model selections and performance comparisons. Tables 3 and 4 show that the proposed dataset and features performed efficiently in all the classifiers tested. Furthermore, the obtained evaluation metrics were strong and consistent across each testing approach, confirming that the employed features and representation are appropriate for detecting this attack. Some classifiers already achieved excellent results; therefore, not all need improvements. However, researchers can try to improve those that did not attain high values by modifying the features by using feature selection techniques to reduce them in order to enrich the features, extract extra features, or optimize the classifiers. Furthermore, because these classifiers were used with their default settings, parameter adjustment/tuning can enhance the evaluation metrics achieved for the classifiers that did not attain high values. Based on these findings, it was confirmed that the proposed dataset fulfilled the final requirement of an effective dataset. Furthermore, by applying any suggested classification methods in the optimization zone to the dataset and matching the classification abilities of the approach to actual outcomes, as illustrated in Tables 3 and 4, this dataset can be employed for a classification evaluation of any proposed method in the optimizing field. Furthermore, the provided dataset may be adopted to know the differences between normal requests and HTTP-GET flood DDoS attack distinctions on CCEs to provide additional detection systems. Even if available, publicly accessible datasets are often altered to protect users' anonymity, limiting complete data access.