## 4 Experiments and Their Outcomes
### Answer to Third RQ  
To assess the CNN + BiLSTM model's effectiveness in predicting DDoS attacks from historical traffic, it was compared to various deep learning (DL) models, including CNN, long/short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and BiLSTM. Table 9 summarizes the findings. * CNN + BiLSTM vs. CNN: the purpose of this experiment was to evaluate the suggested CNN + BiLSTM model against a single-layer CNN model in terms of effectiveness. As shown in Table 9, the CNN model demonstrated lower precision, recall, F1-score, and accuracy. CNN is ranked low since it (i) lacks information about the text's sequence context and (ii) needs a large data set to give an enhanced classification performance. * CNN + BiLSTM vs. LSTM: this investigation compared the suggested CNN + BiLSTM model's effectiveness against that of an LSTM model. As shown in Table 9, the CNN model demonstrated lower precision, recall, F1-score, and accuracy. LSTM models retain only previous contextual knowledge and discard upcoming contextual information, which would aid in comprehending the meaning of the reviewed text. As such, it performed the worst of all the models. * CNN + BiLSTM vs. BiLSTM: this investigation compared the proposed CNN + BiLSTM model against a BiLSTM model in terms of predicting court judgments from previous legal data. As shown in Table 9, the BiLSTM model performed worse in terms of precision, recall, F1-score, and accuracy. A BiLSTM model's principal aim is to store contextual information for both forward and reverse directions in a sequence. BiLSTM is ranked low due to its ineffectiveness in extracting features. * CNN + BiLSTM vs. CNN + LSTM: the purpose of this experiment was to evaluate the suggested CNN + BiLSTM model against a CNN+LSTM model. The CNN + LSTM model underperformed in terms of precision, recall, F1-score, and accuracy, as shown in Table 9. This is because the unidirectional LSTM layer is ineffective at retaining subsequent contextual information, leading to suboptimal efficiency. * CNN + BiLSTM vs. RNN: the aim of this experiment was to evaluate the suggested CNN + BiLSTM model against an RNN model in terms of effectiveness. As shown in Table 9, the RNN model achieved suboptimal performance in terms of precision, recall, F1-score, and accuracy. Due to the fact that RNN models are unable to manage exceptionally long-term sequencing, they would not retain information for an extended length of time. As a consequence, the RNN model produced suboptimal outcomes. * CNN + BiLSTM vs. CNN + RNN: the purpose of this experiment was to contrast the suggested CNN + BiLSTM model against a CNN+RNN model. As seen in Table 9, the CNN + RNN model performed poorly in terms of precision, recall, F1-score, and accuracy. This is because RNN models do not retain context data over extended periods of time. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline \multirow{6}{*}{Different models of deep learning} & CNN & 75 & 74 & 75 & 73 \\ \cline{2-5}  & LSTM & 72 & 76 & 72 & 71 \\ \cline{2-5}  & BiLSTM & 73 & 75 & 73 & 71 \\ \cline{2-5}  & CNN + LSTM & 75 & 76 & 75 & 75 \\ \cline{2-5}  & RNN & 79 & 83 & 79 & 79 \\ \cline{2-5}  & CNN + RNN & 70 & 76 & 70 & 69 \\ \hline \multirow{6}{*}{Proposed model (without FS)} & Proposed CNN + BiLSTM & 83 & 84 & 83 & 83 \\ \cline{1-1} \cline{2-5}  & Proposed model (without FS) & & & & \\ \cline{1-1} \cline{2-5}  & Proposed CNN + BiLSTM & 94.52 & 94.74 & 92.04 & 93.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the proposed effectiveness of the algorithm to that of DL models. The above comparison tests show that the suggested CNN + BiLSTM model outperforms other deep learning models (LSTM, GRU, CNN, BiLSTM, and RNN) in terms of precision, recall, F1-score, and accuracy. This resulted in an increase in classification accuracy when two deep learning models, namely CNN and BiLSTM, were combined along with feature selection.