## 5 Results and Discussion
### Results and Discussion of Base Classifier Models
#### 5.3.4 Comparison of Results with Published (Base)  
We compared the result of the proposed system with some recent related works using the DDoS attack SDN dataset and UNSW2018 BoTiO (Table 6). The obtained results showed that the model achieves very high classification results. For the UNSW2018 dataset, the proposed model achieves 100% in all metrics and a very low loss, about 3.98 \(\times\) 10\({}^{-4}\) for training and 3.3 \(\times\) 10\({}^{-3}\) for validation. Table 6 proves that our model outperforms [PERSON] et al. [22] in all metrics using the same dataset (UNSW 2018). [PERSON] et al. [27] and [PERSON] et al. [28] had models that achieved an accuracy of 99.99%, whereas our model achieves 100% accuracy. Our model outperforms their models in all metrics. Although it had high accuracy, they showed lower precision, recall, and F1-score for normal flows. So, our model can accurately detect and recognize normal and abnormal flows since it shows 100% in all metrics.  
Meanwhile, for the DDoS attack SDN dataset, our system gains an accuracy of up to 99.99% in training and 99.923% in validation and achieves 100% in precision, recall, and F1-score. Also, the model training and validation losses are very low, about 3.98 \(\times\) 10\({}^{-4}\) for training and 3.3 \(\times\) 10\({}^{-3}\) for validation. Our model outperforms all proposed models by [PERSON] et al. [29] in all factors. Experimental results reveal that our proposed model has a high feature extraction ability and high performance in detecting attacks. All performance metrics indicate that the proposed approach is the most appropriate choice to apply to a real-world flow detection environment.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Val. Accuracy (\%)**} & \multicolumn{3}{c}{**Precision (\%)**} & \multicolumn{3}{c}{**Recall (\%)**} & \multicolumn{3}{c}{**F1-Score (\%)**} \\ \cline{5-10}  & & & & & & & **Normal** & **Attack** & **Normal** & **Attack** \\ \hline Proposed & \multirow{4}{*}{UNSW2018} & CNN-LSTM-autoencoder & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ [PERSON] et al. [22] & & autoencoder & 99.95 & 99.94 & 95 & 99 & 94 & 99 & 95 & 99 \\ [PERSON] et al. [27] & & feed-forward neural & 99.99 & 99.99 & 82.55 & 99.99 & 66.35 & 99.99 & 73.57 & 99.87 \\ [PERSON] et al. & & & & & & & & & \\
[28] & & VMFCVD & 99.99 & 99.99 & 87.72 & 99.99 & 82.55 & 99.99 & 81.97 & 99.99 \\ Proposed & \multirow{4}{*}{DDOS attack} & CNN-LSTM-autoencoder & 99.99 & 99.923 & 100 & 100 & 100 & 100 & 100 & 100 \\  & & SDN Dataset & CNN & 98.74 & - & 98.75 & 98.73 & 98.9 & 98.55 & 98.83 & 98.64 \\ [PERSON] et al. [29] & (Mendely) & LSTM & 95.60 & - & 96.20 & 94.90 & 95.64 & 95.56 & 95.92 & 95.23 \\ [PERSON] et al. [29] & (Mendely) & CNN-LSTM & 99.48 & - & 99.43 & 99.55 & 99.66 & 99.26 & 99.54 & 99.40 \\  & & SVC-SOM & 95.45 & - & 96.71 & 93.75 & 95.40 & 95.51 & 96.05 & 94.62 \\  & & SAE-MLP & 99.75 & - & 99.96 & 99.69 & 99.77 & 99.94 & 99.87 & 99.82 \\ [PERSON] et al. [22] & \multirow{4}{*}{Generated} & \multirow{4}{*}{LSTM-autoencoder} & 97.62 & 97.68 & 98 & 88 & 92 & 97 & 95 & 93 \\ \cline{1-1}  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison results between the proposed CNN-LSTM-autoencoder model and some recent works.