\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Algorithms** & **Datasets** \\ \hline
[15] & 2018 & SVM, DNN & KDD-Cup99, Mininet for SDN \\ \hline
[16] & 2018 & SVM & KDD-Cup99 \\ \hline
[11] & 2019 & SVM, MLP, DT (CART), RF & Scapy tool, Mininet \\ \hline
[30] & 2019 & DT (J48), RF, SVM, KNN & hping \\ \hline
[32] & 2020 & SVM (linear, polynomial) & Scapy tool \\ \hline
[33] & 2020 & K-means++, RF & InSDN \\ \hline
[24] & 2020 & DNN, linear SVM, DT, NB & CICIDS2018 \\ \hline
[34] & 2020 & CNN-LSTM, MLP, SVM & Self-generated \\ \hline
[2] & 2020 & KNN, NB, AdaBoost, DT, RF, & InSDN (self-generated) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of previous studies on the use of ML/DL algorithms for DDoS attack detection in SDN. Figure 2: DDoS attack architecture in SDN [7]. Another observation in Table 1 is that most studies performed a comparative analysis of various ML and DL algorithms, where the focus was to mutually compare conventional algorithms and report the results using well-known performance measures such as accuracy, precision, recall, and F-score. Although such comparative studies give useful insight into the problem, they did not place emphasis on algorithmic improvement or provide further analysis beyond the use of traditional performance measures. Among all studies reported in Table 1, there are only a few that proposed or analyzed advanced ML/DL algorithms (such as hybrid or ensemble algorithms). For example, [PERSON] et al. [33] proposed a modified version of the the K-means algorithm, termed K-means++, and evaluated its performance using the InSDN dataset. Similarly, [PERSON] et al. [34] proposed a CNN-LSTM hybrid and analyzed its performance using a custom-generated dataset. [PERSON] et al. [26] and [PERSON] et al. [20] proposed SVM ensembles and assessed the performance of their proposed algorithms using the CICIDS2019 and KDDCup-99 datasets, respectively. Finally, [PERSON] et al. [22] proposed a hybrid deep learning algorithm that was evaluated on the CICIDS2017 and NSL-KDD datasets. It should be noted that while the aforementioned studies proposed new variants of ML/ DL algorithms, all of them, with the exception of [PERSON] et al. [33], used datasets that did not qualify for testing SDN-based attack detections. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Algorithms** & **Datasets** \\ \hline
[35] & 2021 & RNN, LSTM, GRU & InSDN \\ \hline
[25] & 2021 & KNN, SVM, DT, NB, RF, XGBoost & CIC-DDoS2019 \\ \hline
[17] & 2021 & SVM, DT & KDD-Cup99 \\ \hline
[1] & 2021 & SVC-RF, LR, SVC, KNN, RF, ANN & New dataset \\ \hline
[18] & 2022 & KNN, logistic regression, DT & KDD-Cup99 \\ \hline
[19] & 2022 & SVM, GLM, NB, DA, FNN, & 1999 DARPA, \\  & & DT, KNN, BT & InSDN, DASD \\ \hline
[36] & 2022 & LR, NB, DT & Mininet for SDN \\ \hline
[26] & 2022 & RF, KNN-SVM,NB & CIC-DDoS2019 \\ \hline
[20] & 2022 & SVM (Ensemble), DT (48), KNN & KDD-Cup99 \\ \hline
[9] & 2022 & SVM, KNN, NB, RF and DT & NSL-KDD \\ \hline
[27] & 2022 & PCA, KPCA, LDA, DT, RF & CIC-DDoS2019 \\ \hline
[37] & 2023 & SVM, NB, MLP & Mininet \\ \hline
[38] & 2023 & SVM, DT, Gaussian NB, RF, & Mininet \\  & & extra tree classifier, ANN & \\ \hline
[21] & 2023 & RNN, GRU, MLP, LSTM & CICIDS2017, \\  & & CIC-DDoS2019 \\ \hline
[22] & 2023 & GRU, hybrid DL & CICIDS2017, NSL-KDD \\ \hline
[39] & 2024 & LR, LDA, NB, KNN, & \\  & & CART, AdaBoost, RF, SVM & InSDN \\ \hline
[23] & 2024 & KNN & CICIDS2017, \\  & & CICIDS2018, & CIC-DDoS2019 \\ \hline
[28] & 2024 & MLP & CIC-DDoS2019 \\ \hline
[31] & 2024 & XGBoost-SHAP & CIC-IOT-2023 \\ \hline
[40] & 2024 & SVM, random forest, KNN & Dataset by [PERSON] et al. [1] \\ \hline
[29] & 2025 & SHAP, CNN-BiLSTM, & InSDN, \\  & & AE-MLP, CNN-MLP & CIC-DDoS2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._As observed in the above discussion, it turns out that there are only two studies by [PERSON] et al. [1] and [PERSON] et al. [2] where SDN-specific datasets were developed that are available in the public domain.