## 2 Background and Related Work  
DDoS attacks, which have been extensively studied, are among the most prevalent and serious security concerns. A variety of projects are now using SDN-based security measures to prevent such assaults. Here is a summary of some important works in this area:  
[PERSON] et al. [7] proposed an SDN-based solution to combat DoS and DDoS attacks in IoT networks. The solution relies on OpenState, a potential technique for network monitoring, as it does not send packets to the controller. The SDN controller can identify DoS and DDoS attacks based on their entropy levels, as has been shown. The impact of this parameter was better understood by evaluating the application's performance in three different scenarios. The first scenario measures the attack's bandwidth and entropy values in a generic testbed, while the second and third scenarios are more focused on IoT scenarios. According to the experimental findings, an attack can be detected by comparing the entropy levels of different aspects. [PERSON] et al. [8] presented a method for improving DDoS protection and data management security in SDN-enabled smart cities. A DDoS attack defense approach based on traffic classification was presented in this paper. The authors used a software-defined network function, virtualization design, and traffic categorization approach to improve SDN flexibility and load against DDoS attacks. According to the experimental results, the suggested approach can not only identify DDoS attacks quickly, but it can also correctly pinpoint the origins of DDoS attacks. As a result, SDN controllers are less vulnerable to attack, and the system is more efficient. [PERSON] et al. [9] used the SDx paradigm to propose an overall framework for software-defined internet of things (SD-IoT). The controller pool in the proposed system consists of SD-IoT controllers, switches connected to an IoT gateway, and IoT devices. Researchers developed a method for identifying and mitigating DDoS attacks using the SD-IoT architecture. The similarity measured by the cosine of the packet message rates at the boundary SD-IoT switch ports is employed in the algorithm to assess whether DDoS attacks are present in the IoT. Experiments conducted in this study demonstrate that the suggested algorithm performs well and that the proposed framework can be easily adapted to increase IoT security for diverse and susceptible devices. [PERSON] et al. [10] proposed an efficient DDoS attack detection system based on DDoS detection techniques. According to the findings provided in this work, DDoS attacks may be detected and mitigated in a large-scale network that includes a smart city based on SDN architecture. This framework can meet all DDoS attack detection and mitigation requirements. This study first surveys and classifies SDN-based DDoS attack identification and resolution systems based on the detection approach. Second, the authors presented an SDN-based DDoS Framework, which utilizes the SDN's properties for network security. Applications for smart cities may be secured with the framework presented here. Application identification in network architecture has been advocated by [PERSON] et al. [11]. This can help to prevent distributed denial-of-service (DDoS) attacks by restricting data flow. [PERSON] et al. [12] proposed a DDoS attack identification technique that relies on network traffic statistics. The controller monitors the amount of traffic and the frequency of specific events that are associated with DoS attacks. [PERSON] et al. [13] introduced a technique based on self-organizing maps. Researchers have developed a way to detect fraudulent internet patterns using this method. [PERSON] et al. [14] discussed the current state of prior network behavior and analysis. They developed a flow count identification technique for anomaly detection. This approach can be used to detect anomalous traffic flows that are associated with DDoS attacks.