# Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment
## 2 Related Works  
### Comprehensive Overview  
This section delves into contemporary and extensively employed methodologies for detecting DDoS attacks by applying ML techniques. An exhaustive comparison of the recent related works on improving ML-based DDoS detection is summarized in Table 1. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref** & **Year** & **Technique** & **Optimizer** & **Feature Selection** & **Dataset** & **Network** \\ \hline [PERSON] and [PERSON] [9] & 2023 & DNN & NS & Fusion of statistical importance using standard deviation & NSL-KDD, UNSW\_NB-15, CIC-IDS-2017 & NS \\ \hline [PERSON] et al. [4] & 2023 & Autoencoder and XGBoost & Hyperparameter tuning & SHAP & SDNIoT, SDN-NF-TJ & SDN-IoT \\ \hline [PERSON] et al. [10] & 2022 & Seven ML, four DL, five unsupervised & NS & 15 individual methods and one ensemble method & UNSW\_NB-15 & NS \\ \hline [PERSON] et al. [11] & 2022 & Bayesian optimize-DT optimization & MRMR & Self-generated VMANET & SD-VANET \\ \hline [PERSON] and [PERSON] [12] & 2022 & LR, DT, SVM, KNN, GNB, RF, XGBoost, ANN, CNN & GridSearchCV and random sampling is, DT, RF and XGBoost & Chi2, IG, merged Chi2-IG ranking ML classifiers; that is, DT, RF and XGBoost & KDD Cup 99, UNSW\_NB-15 & NS \\ \hline [PERSON] and [PERSON] [13] & 2022 & Extreme ML & Memory optimization & Hybrid & CICDDoS-2019 & NS \\ \hline [PERSON] et al. [14] & 2022 & SVM, NB, FNN, KNN, GLM, DT, BT & DA, BT counter within a time period & Single feature based on OASD, InSDN, Self-generated \\ \hline [PERSON] and [PERSON] [15] & 2022 & KNORA-E, KNORA-U tuning & Hyperparameter tuning & SHAP & CICDDoS-2019 & NS \\ \hline [PERSON] et al. [16] & 2022 & MLP-GA, MLP, NB, RBF network & NS & IG, Gain ratio, Chi2, ReliefF, ensemble method & NSL-KDD & NS \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of recent literature. In [9], [PERSON] and [PERSON] introduced a model that harnessed statistical importance, specifically standard deviation, to facilitate the selection of features within a deep neural network. Their pioneering work sought to bolster the efficiency of intrusion detection and classification. To assess the efficacy of their approach, they conducted evaluations by using three diverse datasets: NSL-KDD, UNSW_NB-15, and CIC-IDS-2017. In [4], the authors presented an optimized approach for precisely identifying DDoS attacks within Software-Defined Internet of Things (SDIoT) networks. This approach harnessed the power of Autoencoder and XGBoost algorithms' power, meticulous feature selection, and hyperparameter-tuning optimization. The method's robustness was evaluated through rigorous testing by using two specific SDN-IoT datasets: SDN-NF-TJ and SDNIoT. The authors in [10] presented a DDoS-detection analysis by using seven ML algorithms, four DL, and five unsupervised models. They evaluated the performance of 15 methods to select features according to the UNSW_NB-15 dataset. The authors of [11] presented an approach to address the security of the SDN paradigm compared to the traditional Vehicular Ad Hoc Networking (VANET); they simulated a dataset and proposed a system based on the Minimum Redundancy Maximum Relevance process to deal with DDoS attacks. This approach was implemented in a Bayesian optimization-based decision tree classifier. In [12], the authors evaluated the performance of detecting DDoS attacks by using logistic regression, a decision tree classifier, linear support vector machine, k-nearest neighbors, Gaussian Naive Bayes, Random Forest Classifier, XGBoost, ANN, and CNN. They also used hyperparameter optimization and applied various techniques to select features on KDD Cup 99 and UNSW-NB15. An extreme learning machine was used in [13] to ameliorate the performance related to the DDoS-attack-detection system; the authors