# Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment
## 2 Related Works
### Key Findings  
The realm of detecting DDoS attacks has witnessed a substantial surge in research endeavors. These investigations have honed in on the strategic fusion of SDN infrastructures and the capabilities of DL models within diverse domains. This collective exploration has produced a trove of invaluable insights, each adding a distinctive layer to the evolving landscape of DDoS detection. Let us delve into these pivotal findings:  
* Network-Specific Innovations: A significant portion of this research body has been intricately tailored to address the intricate nuances of specific network types. Notably, SDN and SD-VANET networks have emerged as focal points of interest. Researchers have recognized the importance of customizing DDoS-detection approaches to suit these specialized network environments. These tailored strategies delve into the unique characteristics and challenges that SDN and SD-VANET networks present, paving the way for more effective ML-based DDoS detection within these domains.
* Feature Selection and Deep Learning's Prowess in SDN: In the context of SDN environments, a remarkable discovery has centered around the power of feature-selection techniques. These methodologies meticulously sift through data to pinpoint the most pertinent features, substantially elevating the performance of ML-based DDoS-detection systems. Furthermore, DL techniques have come under the microscope, with researchers diligently assessing their contribution to SDN's overarching DDoS-detection framework. DL models have showcased an unparalleled aptitude for capturing intricate data patterns and relationships, positioning them as valuable assets in the arsenal against DDoS threats.
* Fine Tuning via Hyperparameter Optimization: Research efforts have introduced a meticulous fine-tuning mechanism to optimize the performance of DDoS-detection systems. Hyperparameter-optimization techniques, including Bayesian optimization and GridSearchCV, have played a central role in systematically adjusting the settings of ML models. This systematic calibration process bolsters the accuracy and efficiency of these models, ensuring that they are finely attuned to the intricate nuances of DDoS detection.
* The Rise of Hybrid Deep Learning Models: A prominent revelation in this landscape pertains to the emergence of hybrid DL models. These innovative architectures skillfully merge multiple DL frameworks or combine DL with traditional ML techniques. The overarching objective is to enhance the interpretability and robustness of DDoS-detection systems. By amalgamating diverse models, these hybrid entities provide a wealth of insights and information that guide the final decision making. This synergy augments the accuracy of DDoS detection and crystallizes the findings, making them more comprehensible and actionable.  
In essence, the meticulous research endeavors in DDoS detection have coalesced into a compendium of strategies, each meticulously tailored to the nuances of SDN environments and propelled by the profound capabilities of DL models. These discoveries encompass network-specific tailoring, the precision of feature selection, the finesse of hyperparameter optimization, and the innovation of hybrid models. Collectively, these findings fortify the security of networks and equip them to confront the ever-evolving specter of DDoS attacks with heightened resilience.