# Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
## 2 Literature Review
The authors of [39] provide a new approach presented in this study. With the presence of these types of malicious nodes, attacks can be classified as active and inactive. In [40], the authors propose to identify the DDoS attack and mitigation model using the feature selection method. In the presented study, the network traffic is primarily analyzed according to the Hellinger degree. When a certain distance is detected, all data packets are analyzed and classified into two categories based on the selected segmentation factor, such as DDoS and official application groups [41]. The authors have addressed the problem and developed a secure system for these programs. The experts of [42] proposed a botnet detection method that can manage multiple datasets and also detect botnets in the network. In [43], researchers addressed the need to prevent DDoS attacks by defining and demonstrating a mixed identification model by introducing an advanced and effective method to identify and effectively distinguish flooding in a hot crowd. In addition to introducing a multi-level classification method based on the presented set of entropy-based features with machine learning divisors to improve the low visibility and accuracy, the authors of [44] also introduced a set of novel entropy-based symbols to help reliably detect DDoS attacks. In [45], researchers discussed four important network protection schemes against end-to-end network attacks, end-to-end, victim, and distributed schemes with a focus on two innovative models, Gossip and D- WARD. In [46], the authors introduced a reduction method based on the fuzzy control system. It looks like inserting two new matrices. In [47], the researchers presented a novel selection algorithm, Dynamic Ant Colony System, with a choice of three levels of renewal function. The presented method uses different levels of pheromones to make the ants stronger. The proposed method by the authors of [48] is contrasted with a different hybrid algorithm that is provided with 10-fold cross-validation. The proposed method outperforms existing methods in terms of accuracy, detection rate, and false alarm rate, according to the database-based test results of KDD CUP 99. In [49], the experts proposed a new method to mark a packet that could be forwarded from the attacker's side to the victim's side. It allows the victim to ensure the necessary protection for internet service providers (ISPs). In the manuscript, [50], the authors propose a defense system called SkyShield. This scheme uses a graphical data framework to identify and mitigate DDoS attacks at the application level. First, they proposed new split calculations in two graphs that improve the effect of network dynamics and increase the accuracy of detection. Second, they used an atypical graph to help identify the malicious predators of a persistent attack. In [51],the researchers proposed the concept of a system of experts. This program automatically resets security apps about incoming traffic. To achieve this, it is proposed to use a model, reasoning, and performance-based loop (LRA-M). In this case, it describes the structures of the corresponding system and defines its building blocks. In [52], the authors used a state-of-the-art SDN model, employing a new method for DDoS detection and mitigation known as State Sec. They demonstrated the benefits of this type of method, as shown in Figure 3.  
The authors of [53] analyzed a number of current machine learning (ML) research projects that spread SDN for NIDS implementation. It was discovered that deep learning techniques had been looked into for the creation of SDN-based NIDS. Key steps of DDoS detection and mitigation in software-defined networking are shown in Figure 3. A brand-new authentication method was put forth in [53] as a defence against DDoS assaults on approved domain name servers. For duplicate resolvers, the solution employed the DNAME record to sign the domain redirection guide and then correctly reroute traffic to their downstream query domains.