# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 2 Related Work
### Hybrid Deep Learning Techniques
The low-complexity advantages of information entropy were combined with the high accuracy of the CNN algorithms, thereby facilitating DDoS attack detection in the SDN controller and guaranteeing the security of the SDN network. Ref. [19] also conducted a similar study to [18], whereby information entropy was combined with a CNN algorithm to detect DDoS attacks. In their study, Ref. [19] used information entropy to see suspicious ports and components in coarse granularity, whereas CNN was adopted to distinguish legitimate and attack traffic. Findings from the research revealed high accuracy in detecting the anomaly traffic at 98.98%, which underscored the robustness of combining information entropy and CNN techniques in detecting attack traffic. Therefore, a similarity between [18,19] emerged from the fact that both studies advocated combining information entropy with CNNs to detect attack traffic within SDNs. As a result, there was better performance and accuracy regarding mitigating DDoS attacks. Further analysis, however, indicated that the shortcoming of reliance on deep learning algorithms on their own arose from high training costs and low efficiency despite their high accuracy. Analytically, such findings indicate a need to identify more alternative solutions to reduce the disadvantages of deep learning algorithms. Ref. [20] reiterated [21] by demonstrating that integrating a deep learning algorithm enhanced IDS systems. Ref. [22] further demonstrated the effectiveness of the Stacked Autoencoders (SAE) deep learning algorithm combined with a Snort IDS in optimizing the detection accuracy of DDoS attack detection within SDN environments. By implementing the hybrid algorithm, the study observed a high accuracy of 95%. Additionally, hybrid deep learning algorithms have shown promising results in detecting low-rate DDoS attacks in software-defined networks (SDNs). These attacks are characterized by a low volume of traffic but a high frequency, making them difficult to detect using traditional approaches such as threshold-based systems [3]. Another hybrid deep learning method is the integration of a CNN with a deep belief network (DBN), as presented by [23]. This approach achieved a detection rate of 96.7% and a false positive rate of 1.2% on a simulated SDN dataset. Finally, the analysis also indicated that deep learning algorithms could better detect DDoS attacks in SDN environments [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. The research emerged that deep learning techniques outperformed classical machine learning algorithms, as they did not involve human interaction to improve their performance but instead relied on artificial neural networks [24]. The evaluation also demonstrated that hybrid deep learning techniquesoutperformed single algorithms even in scenarios where the models comprised deep learning algorithms. Therefore, in this article, the superior performance of our hybrid approach in detecting DDoS attacks in SDN environments is examined further to protect SDN environments. In particular, the research focused on developing a model comprising deep learning algorithms to ensure high performance in detecting DDoS attacks within SDN environments.