# Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment
## 2 Related Works
### Comprehensive Overview
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref** & **Year** & **Technique** & **Optimizer** & **Feature Selection** & **Dataset** & **Network** \\ \hline [PERSON] and [PERSON] [17] & 2022 & J48 classifier & NS & Combination of IG and correlation (CR) & CICDDoS-2019, KDD Cup 1999 & NS \\ \hline [PERSON] [18] & 2022 & Autoencoder and LSTM & NS & IG, RF & InSDN, CICIDS2017, CICIDS2018 & SDN \\ \hline [PERSON] & 2022 & DNN, CNN, LSTM & NS & IG & CICDDoS-2019 & NS \\ \hline [PERSON] et al. [20] & 2022 & SVM, Guard, KNN & Threshold tuning & A proposed feature-selection method & IMPACT CAIDA & IoT \\ \hline [PERSON] et al. [21] & 2022 & Majority Voting & NS & One from filter, wrappers, and embedded methods & UNSW\_NB-15 & NS \\ \hline [PERSON] and [PERSON] [22] & 2022 & Six ML & NS & ANOVA & Self-generated SDN \\ \hline [PERSON] et al. [23] & 2022 & RF, LR, SVM, NB, MLP & NS & A proposed multiaspect ensemble feature selection & CICDDoS-2019, InSDN & SDN \\ \hline [PERSON] et al. [3] & 2022 & Eight ML & NS & Adaptation and dimensionality reduction & SDN dataset & SDN \\ \hline [PERSON] and [PERSON] [24] & 2021 & LR, GB, DT, KNN, SVM & Hyperparameter tuning & Hybrid based on Spearman’s correlation and RF & CICDDoS-2019 & NS \\ \hline [PERSON] and [PERSON] [25] & 2020 & LR, KNN, GNB, RF, SVM & NS & One from filter, wrappers, and embedded methods & CICIDS2017 & NS \\ \hline [PERSON], et al. [26] & 2020 & SVM, NB, ANN, KNN & NS & One from filter, wrappers, and embedded methods & Self-generated SDN \\ \hline [PERSON] and [PERSON] [27] & 2019 & C4.5 & NS & Hybrid based on filter and wrapper methods & Self-generated SDN \\ \hline [PERSON] et al. [28] & 2019 & Combining edge-based method with a cloud-based one & Multiparameter edit distance & Self-selected based on WSN behavior & Self-generated WSN \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)suggested a method based on memory optimization and a hybrid approach for selecting features. They conducted the experiments by using the CICDDoS-2019 dataset. In the SDN infrastructures context, the article [14] simulated an SDN environment to evaluate DDoS-attack detection in SDN by using supervised learning techniques, and 1999 DARPA, the DDoS-attack SDN dataset (DASD), and InSDN datasets were also used for this evaluation. In [15], the authors employed the SHAP-feature weight within recursive feature exclusion by using five base classifiers to detect DDoS attacks. In [16, 25], many experiments were conducted to evaluate feature selection's impact on Ensemble Learning techniques' performance by combining several ML models to improve the overall performance. It is based on the principle that multiple models working together often achieve better performance than a single model alone. In [17, 24, 27], the authors proposed hybrid feature-selection methods to enhance the DDoS-detection systems. Selecting features according to the Information Gain technique was also evaluated in [18, 19] to deal with DDoS attacks. The authors of [20] proposed a method to select features and detect DDoS attacks based on flow classification and threshold tuning for optimization. The authors of [26] generated a dataset in an SDN-simulated network and evaluated the performances against DDoS attacks through different methods of selecting features and ML models. The paper [21] evaluated selecting-features methods by using Majority Voting and the UNSW_NB-15 dataset. The authors of [22, 23] exploited the SDN infrastructures to evaluate the DDoS-attack-detection systems. In [3], the authors investigated a methodology based on dataset understanding, feature modeling, and dimensionality reduction to improve DDoS detection via ML-based systems in the SDN environment. The approach introduced by [PERSON] et al. [28] leverages edge-data analysis combined with cloud data analysis to autonomously detect anomalies within heterogeneous sensor networks.