## 4 Experiments and Their Outcomes
### Significance Testing  
To determine the study's significance, two experiments were undertaken (see Table 12). If the CNN + BiLSTM (DL) model outperformed the SVM(ML) model statistically, it did not occur randomly.  
After randomly choosing 100 records from the dataset, the individual records were classified using the CNN + BILSTM (DL) and KNN classifiers (ML). Two hypotheses were tested in the experimental context:  
Hnull: The error rates across both models are identical.  
Halternate: The error rates of both models are significantly dissimilar.  
Equation (12) gives [PERSON]'s chi-squared statistic test:  
\[\chi^{2}=\frac{\left(\left|x-y\right|-1\right)^{2}}{\left(x+y\right)} \tag{12}\]  
The cells x and y were used to generate discordant test statistics, with \(1\) representing the degree of freedom and \(\chi^{2}\) representing chi-squared.  
Analysis  
Table 9 demonstrates the CNN + BILSTM model's utility, demonstrating a significant improvement in predicting DDoS attacks from historical traffic data, with an accuracy of 94.52 percent. The utility of the SVM model is seen in Table 8, since it scored poorly on all estimation metrics: precision, recall, F1-score, and accuracy. According to the significance test, the disparity between both the DL model (CNN+BiSLTM) and the ML model was substantial (SVM). Using continuous correction, the \(p\)-value for [PERSON]'s statistic test was computed. The Chi-squared coefficient was 2.2, with one degree of freedom and a \(p\)-value of 0.135 for two-tailed analysis. A \(p\)-value less than 0.5 validated the alternative hypothesis and disproved the null hypothesis. As a consequence, the suggested model  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **Accuracy** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **Decision** \\ **Macro** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **Decision** \\ **Macro** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean F-I** \\ **Decision** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Mean F-I** \\ **Macro** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } \\ \hline Random Forest & 82 & 0.06 & 83 & 0.05 & 83 & 0.06 & 83 & 0.07 \\ \hline SVM & 72 & 0.06 & 72 & 0.06 & 72 & 0.06 \\ \hline XGBoost & 84 & 0.07 & 83 & 0.06 & 83 & 0.06 & 83 & 0.07 \\ \hline CNN & 86 & 0.07 & 87 & 0.05 & 86 & 0.05 & 83 & 0.06 \\ \hline BiLSTM & 85 & 0.06 & 85 & 0.05 & 83 & 0.05 & 84 & 0.05 \\ \hline CNN + BILSTM (proposed) & 93.11 & 0.05 & 91 & 0.04 & 91 & 0.05 & 92 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Cross validation of the proposed system contrasted with different classifiers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Correct Classification** & **Misclassification** \\  & **with SVM** & **with SVM** \\ \hline Correct classification & 60 & 4 & 64 \\ CNN + BILSTM & 14 & 22 & 36 \\
**Total** & 74 & 26 & 100 \\ \hline \hline \end{tabular} With a two-tailed \(p\) value of 0.135 and a degree of freedom of 1, the value of chi-squared is 2.2. The alternative hypothesis is accepted, whereas the null hypothesis is rejected (both models are statistically significant).  
\end{table}
Table 12: Significant differences between the SVM (ML) and CNN + BILSTM (DL) models.  
with word embedding outperformed the SVM model based on conventional features by a statistically significant margin. This demonstrates how the addition of word representation characteristics enhanced the CNN + BILSTM model's resistance against DDoS assaults using historical traffic data.