# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
###### Abstract  
This paper proposes a hybrid deep learning algorithm for detecting and defending against DoS/DDoS attacks in software-defined networks (SDNs). SDNs are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic. To address this problem, we developed a hybrid deep learning approach that combines three types of deep learning algorithms. Our approach achieved high accuracy rates of 99.81% and 99.88% on two different datasets, as demonstrated through both reference-based analysis and practical experiments. Our work provides a significant contribution to the field of network security, particularly in the area of SDN. The proposed algorithm has the potential to enhance the security of SDNs and prevent DoS/DDoS attacks. This is important because SDNs are becoming increasingly important in today's network infrastructure, and protecting them from attacks is crucial to maintaining the integrity and availability of network resources. Overall, our study demonstrates the effectiveness of a hybrid deep learning approach for detecting DoS/DDoS attacks in SDNs and provides a promising direction for future research in this area. hybrid deep learning; DoS/DDoS attacks; software-defined networks (SDNs); cyberattacks academie Editor [PERSON]  
6 February 20238 March 20238  
Previous research has explored various approaches to addressing this problem, including non-deep learning solutions such as packet entropy techniques [5] and time-based methods [6]. While these approaches have successfully mitigated DDoS attacks, there is still room for improvement in accuracy and efficiency. In particular, the adoption of deep learning algorithms has the potential to significantly improve the effectiveness of security measures for SDN networks. For example, the GA [7] and RNN [8] algorithms have been implemented as unique deep learning approaches to addressing DDoS attacks in SDN networks. This was a significant contribution, as low-rate DDoS attacks can be particularly difficult to detect due to their low volume and slow rate of traffic. By considering both types of attacks, our approach was able to provide a more comprehensive and robust analysis of the security challenges facing SDN networks. One of the key differences between our approach and other existing approaches was that while they may have focused mainly on detecting volumetric DDoS attacks, our approach considered both volumetric and low-rate DDoS attacks in the context of SDN networks. Additionally, our approach employed a hybrid deep learning approach that combined multiple algorithms and datasets in order to improve the accuracy and efficiency of our analysis. This was in contrast to other approaches that may have relied on a single algorithm or dataset, which may be less robust and less able to handle the complexity and variability of real-world data. By using a hybrid approach, we aimed to provide a more reliable and effective solution for detecting and defending against DDoS attacks in SDN networks. This research makes a significant scientific contribution to the field of DDoS attack detection and defense in SDN networks by proposing a hybrid deep learning approach that combines multiple algorithms and datasets to improve accuracy and efficiency. This approach is novel in its ability to detect both volumetric and low-rate DDoS attacks and to handle the complexity and variability of real-world data. The results of this study demonstrate the effectiveness of deep learning techniques in addressing the security challenges facing SDN networks and highlight the potential for further improvements in this area. Overall, this research contributes to the development of more reliable and effective security measures for SDN networks, which are critical for ensuring the availability and integrity of networked systems in today's interconnected world.