## 6 DDoS Attack Detection and Mitigation Techniques  
Cyber, digital, and IoT infrastructures have been seriously threatened by distributed denial-of-service (DDoS) assaults. These attacks have the power to severely disrupt these infrastructures operating while they are restricted to traditional mitigation solutions. Recently, several detection and mitigation techniques were proposed on the basis of new networking paradigms such as SDN, new artificial intelligence methods like machine learning models, as well as blockchain technology.  
A collaborative technique to detect and mitigate DDoS flooding attacks on SDN was proposed in [46]. For this purpose, this solution merges the Snort rules with the sflow-RT application. Several Ryus SDN controllers can exchange DDoS detection and mitigation rules using Redis simple message queue (RSMQ). [PERSON] et al. [47] considered blockchain technology to defend against DDoS attacks and unauthorized access. The switches in this system are registered and checked with no proof of knowledge. In the blockchain, they are additionally verified using a consensus system based on voting. To recognize aberrant flow traffic, a [PERSON] deep learning engine is also used.  
Authors in [48] introduced a blockchain-based framework for a multilevel DDoS mitigation technique (ML-DDoS) to safeguard IoT devices. The suggested system's main idea is to employ a device-based verification mechanism powered by blockchain to keep fraudulent devices out of IoT environments. Hyperledger Caliper was used to create the presented framework.  
In order to deal with slow-rate DDoS threats, a scalable SDN-based framework that combines an intrusion detection system (IDS) based on deep learning (DL) and an intrusion prevention system (IPS) based on deep reinforcement learning (DRL) was proposed in [49]. This framework includes scalability aspects like traffic flow sampling and data-plane-basedtraffic monitoring. Besides, OPTIMIST [50] presents another solution that is built around a distributed IDS solution designed for IoT networks with optimal placement. A novel offline training strategy was proposed employing WGAN-generated simulated flows in the IDS module, which is based on an LSTM model. On the basis of a factorization machine, a multifeature DDoS intrusion detection system was developed in [51]. To detect the low rate DDoS attack in this system, flow rule features were first extracted and then used in the factorization machine learning technique. In [52], an IDS for IoT was proposed based on convolutional neural network (CNN) model and metaheuristic optimization algorithms for feature selection.  
Regarding the high importance of DDoS threats, although many researchers addressed this issue, it nevertheless requires genuine attention. The existing work requires a lot of time for attack detection and concentrates mainly on IoT device protection regardless of quality of service (QoS) and/or scalability. The accuracy and precision of some proposed machine learning models could be improved, as well. Thus, in this work, we propose a holistic approach that consider security of the whole IoT network and its scalability, based on the integration of SDN supervision and programmability that considers blockchain to secure distributed multicontroller networks while augmenting the intelligence of these controllers by ANN learning model. A summary of related work is presented in Table 2.