## 1 Introduction
Thus, blockchain technology has been employed to secure several IoT environments [15] and to mitigate DDoS attacks in IoT environments due to its anonymity, decentralization, and auditability [12; 16]. On the other hand, the data plane also presents high vulnerability to DDoS attacks [17]. It is impossible to distinguish between attack traffic and normal traffic during a DDoS attack since they are so similar. They behave quite similarly to typical network packets but in greater quantities and with a greater focus on the target. A malicious assault originating from a small group of nodes is simpler to be detected and stopped. A typical DDoS attack often involves a large number of nodes, and the collective behavior of these nodes dramatically reduces the likelihood of valid requests being completed. IoT devices and SDN switches presenting the data plane nodes in such architecture generate traffic to SDN controllers and help them to detect malicious traffic using machine and deep learning techniques [18]. As a result, they can act appropriately. The learning models are frequently made up of a collection of guidelines, processes, or sophisticated \"transfer functions\" that can be used to find patterns in IoT data related to security incidents as well as identify and forecast behavior [19]. Hence, a data-driven model for IoT security intelligence could be developed using machine learning and deep learning techniques. Indeed, several machine learning techniques, such as classification and regression analysis, clustering, rule-based techniques, feature-optimization techniques, and deep learning techniques based on artificial neural networks (ANN), such as the multilayer perceptron network, convolutional network, recurrent network, etc., can be used to explore IoT data to improve its security [20; 21; 22]. Despite the different solutions and techniques proposed to detect and mitigate DDoS attacks in the IoT context, it still presents a challenging harmful behavior for IoT applications. The adoption of SDN to improve the performance of IoT infrastructure also raised some security issues and could increase the vulnerability of IoT networks. Unlike existing research proposals focusing on applying machine learning techniques or blockchain to detect malicious traffic and/or prevent harmful devices, this paper proposes a holistic security approach that considers SD-IoT infrastructure with multiple controllers to overcome the centralization issue of SDN. Blockchain is used in complementarity with an ANN model to maintain a secure record of the analyzed traffic and the malicious one detected to be shared with the other SD-IoT domains, allowing the prevention against future attacks. The main contributions of this paper are summarized as follows:  
* The study of DDoS attack in IoT and in SDN as well as the capabilities of the latter to mitigate DDoS effects on IoT. * The review of some detection and mitigation techniques based on new technologies and methods. Specifically, we highlighted the effect of blockchain on this field. * Proposition of a holistic approach to secure IoT environment against DDoS attacks. * The implementation and performance evaluation of the ANN model used to guide the SDN controllers to detect and identify malicious traffic in IoT networks. The remainder of this paper is organized as follows. Section 2 presents a background on DDoS attacks in IoT, and this attack in SDN is deeply studied in Section 3. Then, the SDN capabilities to mitigate DDoS effects on IoT are shown in Section 4. In Sections 5 and 6, the blockchain effect on SDN security and DDoS attack detection and mitigation techniques are presented, respectively. Section 7 is dedicated to the description of the proposed solution. In addition, Section 8 focuses on results and discussion. Finally, conclusions are drawn in Section 9.