## 7 Proposed Solution  
In this study, a secure system deployed on a distributed SDN network based on multiple controllers handled with blockchain is proposed to protect IoT environment from DDoS attacks. Traffic features are captured, and an artificial neural network (ANN) model is used to detect malicious traffic/nodes and optimize traffic payload. The results of the detection are used to prevent future attacks. A multi-page blockchain is used as a decentralized firewall to block attacks. The blockchain is also used to optimize traffic routing payload. Additionally, the system uses encryption to secure the data exchange. The proposed solution could be divided into three main parts to mitigate DDoS attacks as depicted in Figure 4:  
(1) _SDN Control plane with security augmented by Blockchain:_ The SDN control plane is the management layer of the SDN network that oversees and configures the forwarding behavior of the switches. The proposed solution augments the security of the control plane by using blockchain technology, which provides transparency and immutability in communication between different controllers, making it easy to trace the source of any attacks and prevent future attacks from occurring. The blockchain acts as a decentralized firewall to block attacks and optimize traffic routing payload. The distributed nature of the blockchain makes it resistant to any single point of failure, ensuring high availability and resiliency in the network. Having multiple controllers in an SDN-managed IoT network facilitates the ability to scale up or down the network by adding or removing controllers as needed. This makes the management of large IoT \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Paper** & **IoT** & **SDN** & **Blockchain** & **ML Method** \\ \hline [PERSON] et al. [46] 2020 & - & \(\times\) & - & - \\ \hline [PERSON] et al. [47] 2021 & - & \(\times\) & \(\times\) & Boltzmann deep learning \\ \hline [PERSON] et al. [48] 2022 & \(\times\) & - & \(\times\) & - \\ \hline [PERSON] et al. [49] 2022 & - & \(\times\) & - & DL \& DRL \\ \hline [PERSON] et al. [50] 2023 & \(\times\) & - & - & LSTM \\ \hline [PERSON] et al. [51] 2020 & \(\times\) & - & - & Factorization \\ \hline [PERSON] et al. [52] 2022 & \(\times\) & - & - & CNN \\ \hline Our proposal 2023 & \(\times\) & \(\times\) & \(\times\) & ANN \\ \hline \hline \end{tabular} Notes: (\(\times\): Considered) (-: Not Considered).  
\end{table} 
Table 2: Summary of DDoS attack detection and mitigation techniques. networks easier and allows for greater flexibility in network design. Such distributed SDN networks allow fault tolerance: in the event of a failure or malfunction of one controller, other controllers can take over the network management tasks, ensuring that the network remains operational and secure. In addition, the distribution of network management tasks allows load balancing that could reduce the burden on one controller and improve network performance. 2. _Artificial Neural Network (ANN) Model:_ The ANN model is used to distinguish normal traffic and devices from malicious ones. The model uses machine learning algorithms to detect anomalies in network traffic and identify DDoS attacks. It analyzes the patterns of network traffic and learns to distinguish normal traffic from malicious traffic. The blockchain serves as a distributed ledger that maintains a secure and tamper-proof record of the traffic type (normal or malicious). By using a blockchain at the control plane level, it is possible to ensure that each switch has a consistent and up-to-date version of rules and that any change or update made to the rules are recorded and verified across the network. Once the ANN model identifies an attack, the blockchain is updated with the information on the attack, and the information is shared securely with the other controllers. Hence, any traffic that matches the characteristics of the attack will be denied to reach network resources, and illegitimate traffic of DDoS attacks could be detected and blocked by firewalls of the data plane.