# Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment
## 4 Proposed Model
### OptMLP-CNN Detector
The OptMLP-CNN model was optimized by using a Bayesian optimizer for hyperparameter tuning and the ADAM optimizer, which adapts its learning rates during training, enhancing the model's convergence and overall performance. The evaluation results indicate that the OptMLP-CNN model consistently achieves high accuracy, precision, recall, F1-score, and AUC across various datasets, showcasing its effectiveness and adaptability in enhancing network security within the dynamic realm of SDN environments. Future work in this domain may explore further model refinement, scalability, real-time adaptation, and expansion into broader cybersecurity applications, leveraging the OptMLP-CNN model's potential to address emerging security challenges effectively. [PERSON] contributed to the research's central idea, including conceptualization, methodology, formal analysis, writing the original draft, reviewing and editing it, as well as handling correspondence. [PERSON] contributed to the methodology, supervision, project administration, validation, visualization, and written work review and editing. [PERSON] participated in the investigation, conducted a formal analysis, and contributed to reviewing and editing. [PERSON] conducted a formal analysis and contributed to reviewing and editing. All authors have thoroughly read and given their approval of the final manuscript. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Two publicly available datasets were analyzed in this study: InSDN [36] and CICDDoS-2019 [37]. **Conflicts of Interest:** The authors declare no conflict of interest. ## Abbreviations

The following abbreviations are used in this manuscript: \begin{tabular}{l l} ADAM & Adaptive Moment Estimation \\ ANN & Artificial neural network \\ ANOVA & ANAlysis Of VAriance \\ BT & Bagging Tree \\ CNN & Convolutional Neural Network \\ DA & Discriminant Analysis \\ DDoS & Distributed Denial of Service \\ DL & Deep Learning \\ DNN & Deep neural network \\ DT & Decision tree \\ EI & Expected Improvement \\ FNN & Feedforward neural network \\ GB & Gradient boosting \\ GLM & Generalized Linear Model \\ GNB & Gaussian Naive Bayes \\ GP & Gaussian process \\ IG & Information Gain \\ IoT & Internet of Things \\ KNN & K-nearest neighbor \\ LR & Logistic regression \\ LSTM & Long Short-Term Memory \\ ML & Machine learning \\ MLP & Multilayer Perceptron \\ MLP-GA & Multilayer Perceptron-Genetic Algorithms \\ MRMR & Maximum Relevance Minimum Redundancy \\ NB & Naive Bayes \\ NS & Not Specified \\ RBF network & Radial Basis Function \\ ReLU & Rectified Linear Unit \\ RF & Random Forest \\ SDIoT & Software-Defined Internet of Things \\ SDN & Software-Defined Networking \\ SGD & Stochastic Gradient Descent \\ SHAP & SHapley Additive exPlanations \\ SVM & Support vector machine \\ tanh & Hyperbolic tangent function \\ VANET & Vehicular Ad Hoc Networking \\ WSN & Wireless Sensor Network \\ XGBoost & Extreme gradient boosting \\ \end{tabular} 

## References

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* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] A comprehensive survey on low-rate and high-rate DDoS defense approaches in SDN: Taxonomy, research challenges, and opportunities. _Multimed. Tools Appl._**2023**, 1-54. [CrossRef]
* [PERSON] et al. (2022) [PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON], Z.E.A. ; [PERSON] Feature Modeling and Dimensionality Reduction to Improve ML-Based DDoS Detection Systems in SDN Environment. In Proceedings of the 2022 19 th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMITP), Chengdu, China, 16-18 December 2022; pp. 1-7. * [PERSON] et al. (2023) [PERSON], [PERSON]; [PERSON]; [PERSON].