## 2 Related Work  
There is a plethora of works on defense of DDoS attacks in SDN using machine learning mechanisms [19; 20; 21; 22]. A more detailed literature review follows. An important survey on mitigation techniques regarding DDoS attacks in SDN is given in [23]. The authors systematically categorized their research into two key domains: one focusing on strategies to counter denial of service (DoS) attacks within Software-Defined Networking (SDN) and the other exploring SDN-centric methods to counteract DoS attacks across diverse networks. In the first category of solutions, they identified six distinct classifications: table-entry, scheduling, architectural, flow statistics, machine learning, and hybrid solutions. Furthermore, the authors conducted a thorough examination of the tools and datasets featured in the reviewed contributions. It delivers a detailed comparative analysis of these approaches, taking into account factors such as the engagement of network devices, network layers, types of DoS attacks, and the specific targets subjected to these attacks. In the paper referenced in [24], a comprehensive analysis of approximately 70 established techniques designed for detecting and mitigating distributed denial of service (DDoS) attacks within Software-Defined Networking (SDN) environments is conducted. These techniques are systematically categorized into four primary groups, incorporating methods based on information theory, machine learning, Artificial Neural Networks (ANNs), and various miscellaneous approaches. Additionally, the paper extensively explores and addresses persistent research challenges, gaps, and issues associated with establishing a secure DDoS defense solution in the realm of SDN. This detailed review is poised to serve as a valuable resource for the research community, aiding the development of more robust and reliable DDoS mitigation solutions tailored for SDN networks. In [25], the paper proposes leveraging the central control features of SDN for attack detection, introducing an efficient and resource-aware solution. Specifically, the paper delves into how DDoS attacks can strain controller resources and presents a method for identifying these attacks by analyzing the entropy variation in the destination IP address. Notably, this approach demonstrates the ability to detect DDoS attacks within the first five hundred packets of the attack traffic. This early detection capability is a significant advancement in proactively identifying and mitigating DDoS threats, enhancing the overall security posture of SDN environments. In [26], the authors highlight the limitations of traditional methods reliant on fixed thresholds and historical data, inhibiting their adaptability to new and evolving DDoS attack scenarios. They propose an innovative approach for detecting DDoS attacks within Software-Defined Networking (SDN) environments. This novel method incorporates three vital components: a collector, an entropy-based module, and a classification stage. Extensive experiments utilizing UNB-ISCX, CTU-13, and ISOT datasets demonstrate that this approach surpasses existing methods in terms of accuracy for DDoS attack detection in SDN environments. Moving to [27], the authors conducted a comprehensive evaluation of the latest advancements in machine learning (ML) and deep learning (DL) methodologies for detecting distributed denial of service (DDoS) attacks within SDN contexts. Their work involved an extensive systematic review focusing on publications utilizing ML/DL techniques to uncover DDoS attacks in SDN networks spanning from 2018 through to early November 2022. This evaluation provides a valuable and updated insight into the evolution and effectiveness of ML and DL techniques in combating DDoS threats within SDN environments. In [28], the authors introduce a DDoS attack detection and defense system that leverages cognitive-inspired computing along with dual address entropy.