# Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
###### Abstract
Among these, deep learning approaches are the most effective at identifying DDoS assaults. The following are shortcomings of the alternative approaches that have been studied to date:  
* Statistical Methods Limitations: The limitations of various DDoS detection approaches have been studied, including statistical and machine learning (ML) methods. Statistical methods are based on past network flow information, which may not accurately describe current network traffic due to evolving hostile network flows. Such techniques rely heavily on user-defined criteria, which need to be able to change dynamically in order to keep up with changes in the network. Statistical techniques such as entropy and correlation require a significant amount of computational effort, making them unsuitable for real-time detection [7]. ML methods work effectively on a small amount of data and determine the statistical properties of attacks before classifying or valuing them. However, they require routine model updates to reflect changes in attack patterns, and certain algorithms can take a very long time to test [8]. * Machine Learning (ML) Limitations: Even when applying ML principles to a tiny quantity of data, it can function quite effectively. The ML first determines the assault's statistical properties before classifying or valuing them. Additionally, it requires routine model updates in order to reflect changes in attack patterns [9]. ML techniques address this problem by decomposing it into manageable subproblems, addressing those subproblems, and then providing the full solution. ML algorithms typically require a short amount of time to train and a considerably longer amount of time to test [10]. DL techniques can effectively identify DDoS attacks, as the data can be classified and the features extracted using DL algorithms, unlike in ML which needs to extract the features in different algorithms before inserting them into the model. In today's security environment, a detection system that can handle data unavailability is a necessity. Although labels for valid traffic are frequently accessible, labels for malicious traffic are less common. DL methods are capable of extracting information from incomplete data [11], and are appropriate for recognizing low-rate assaults. To recognize low-rate assaults,historical data are necessary, which DL techniques use to discover long-term relationships of temporal patterns [12]. As a result, in circumstances where such data are available DL techniques can be very helpful. During the training phase, DL methods perform intricate mathematical operations across a variety of hidden layers and parameters [13]. Quantum computing has shown great promise in a variety of fields, including artificial intelligence (AI), cybersecurity, and medical research. Quantum computing can help AIs to solve more complicated issues by speeding up computation. It can be used with both SML and DL models for quick training or other enhancements. By addressing complicated issues that need vast datasets and are demanding to process, quantum computing can enhance the capabilities of deep learning [14; 15]. Compared to other review studies in the literature, Table 1 illustrates that the majority of these studies have not provided a comprehensive evaluation of the preparation techniques, benefits, and types of attacks used in the analyzed datasets. In contrast, our systematic study presents an extensive review of various deep-learning techniques for detecting DDoS attacks. Through this research, we have identified a gap in the literature, namely, that a comprehensive evaluation of deep learning methods for DDoS detection remains lacking. Our study contributes to addressing this gap by providing a comprehensive review and analysis of the strengths and weaknesses of different deep learning approaches for detecting DDoS attacks. As such, our review provides valuable insights into the current state-of-the-art in DDoS attack detection using deep learning techniques.