## 5 Conclusions and Future Work
### Future Work  
1. To examine the use of various traffic data sets. We want to evaluate the performance of our suggested model on more datasets in the future.
2. To investigate alternative feature selection approaches to the chi-squared assessment, and pre-trained word embedding algorithms, such as autoencoders, Glove, or Fasttext.
3. However, each attack class must be classified independently. We plan to expand our research to include a multi-class categorization system.
4. Furthermore, we will emulate the SDN network with various sorts of settings and attack traffics in order to generate a heterogeneous dataset that accurately represents actual internet traffic.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Study and Technique**} & \multicolumn{2}{c}{**Performance in Our Experiments**} & \multirow{2}{*}{**Performance (Reported)**} \\  & **A (Accuracy), P (Precision), R (Recall)** & \\ \cline{2-3} \cline{2-3}  & **CICDDoS2019** & **CIC-IDS2017** & \\  & **Dataset** & **Dataset** & \\ \hline [PERSON] and [PERSON] [1] & & & \\ Machine Learning (Multiple & 0.75 (A), 0.75 (P), 0.75 (R), 0.75 (F) & 0.78 (A), 0.78 (P), 0.78 (R), 0.78 (F) & 0.85 (A), 0.85 (P), 0.85 (F) \\ Linear Regression) & & & \\ \hline [PERSON] et al. [12] & & & \\ DL Classifier (M.S-CNN) & & & \\ \hline [PERSON] et al. [11] & 0.79 (A), 0.78 (P), 0.79 (R), 0.78 (F) & 0.82 (A), 0.81 (P), 0.82 (R), 0.81 (F) & 0.89 (A), 0.88 (P), 0.88 (F) \\ \hline Proposed system & & & \\ (CNN + BiLSTM & 94.52 (A), 94.74 (P), 92.04 (R), & 93.22 (A), 93.54 (P), 91.01 (R), & N/A \\ with improved FS) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 13: The suggested model and baseline results are compared (A: Accuracy, P: Precision, R: Recall, and F: F1.  
* Additionally, by combining the CICDDoS2019 dataset with hybrid DL, we can give direction to other academics focusing on DDoS vulnerability scanning. When it comes to detecting intrusions and securing software-based networks, it appears that the hybrid DL model with improved FS is an excellent choice due to improved accuracy.
* We intend to create a dataset similar to the CICDDoS2019 dataset in the future by capturing network activity through virtual computers and Internet of things devices. By including DNN and deep learning models in the dataset that will be created, it will be possible to identify real-time DDoS attacks and plan appropriate responses.  
Conceptualization, D.A. and O.B.; methodology, D.A., O.B; software, H.U. and M.Z.A.; validation, M.Z.A., and H.U.; formal analysis, D.A.; investigation, O.B.; resources, D.A., OB, data curation, O.B., and H.U; writing--M.Z.A., D.A. original draft preparation, M.Z.A., D.A; writing--D.A., H.U.; visualization, O.B., M.Z.A.; supervision, D.A.; project administration, D.A.; funding acquisition, D.A. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant No. (RG-95-611-42). The authors, therefore, acknowledge with thanks to DSR technical and financial support.  
Not applicable.  
The data underlying this work can be requested from the corresponding author.  
This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant No. (RG-95-611-42). The authors, therefore, acknowledge with with thanks to DSR technical and financial support.  
The authors declare that they have no conflict of interest.