# Intelligent Traffic Management in Next-Generation Networks
## 3 DL/ML Applications in Software-Defined Networking
### Traffic Classification
#### 3.1.1 Existing Solutions  
In this subsection, we study several works that try to achieve different traffic classification objectives. We have divided traffic classification solutions into two broad categories according to classification level: (i) coarse-grained and (ii) fine-grained. The coarse-grained level classifies the traffic based on the traffic type, while the fine-grained level aims to classify based on the exact application. At the end, we also highlight some other recent approaches. * **Coarse-grained traffic classification** With the intensive growth of online applications, it has become time-consuming and impractical to identify all the applications manually. Therefore, coarse-grained traffic classification aims to identify the QoS classes of traffic flows (e.g., streaming, web browsing, chat, etc.). Coarse-grained traffic classification is used to satisfy the QoS requirements of the network application at the same time. [PERSON] et al. [78] introduced a network traffic classification in the SDN environment where several variants of neural networks were applied to classify traffic flows into different QoS classes (instant messaging, video streaming, FTP, HTTP, and P2P). These classifiers provided 95.6%, 97%, 97%, and 97.6% in terms of accuracy. However, the authors did not mention information on the architecture of the different neural network algorithms. [PERSON] et al. [79] proposed a classification framework based on SDN. It consists of two modules, which are the flow collector and spectral classifier. First, the flow collector module scans the flow tables from SDN controllers to collect traffic flows. Then, the spectral classifier module receives the flow from the flow collector module to create the clusters through spectral clustering. They fixed \(k=6\) as the number of flow classes (HTTP, SMTP, SSH, P2P, DNS, SSL2). Then, to evaluate their results, a comparative analysis with the K-means method in terms of accuracy, recall, and classification time was done. However, the authors did not mention information on the features and preprocessing methods used during their experiments. [PERSON] et al. [59] focused on the identification and selection of flow features to improve traffic classification in SDN. The authors introduced architecture to compute additional flow features and select the optimal subset of them to be applied in traffic classification. This architecture is composed of flow feature manager and flow feature selector. Flow feature manager is responsible for gathering information through the controller and extending this information to complex flow features. Then, the flow feature selector finds the optimal subset of flow features using principal component analysis (PCA) and genetic algorithm (GA), followed by the classifier (SVM) to evaluate this subset. Based on the experimental results, we can reveal some conclusions: this feature selection technique can improve traffic classification and the features generated in this paper can give good accuracy. In the same direction, [PERSON] and [PERSON] [80] compared four classifiers, C4.5, KNN, NB, and SVM. Those classifiers were trained with features selected by their proposed hybrid filter wrapper feature selection algorithm named (_FWFS_). _FWFS_ evaluates the worth of the features with respect to the class, and the wrappers use an ML model to measure the feature subset. The experimental results demonstrate that the selected features through FWFS improve the performance of the classifiers. Recently, the pattern of network traffic has become more complex, especially with the 5G network. This has made simple classifiers unable to provide an accurate model. To solve this issue, researchers in the field of networking started to use ensemble learning for network traffic classification. One of the main advantages of ensemble learning is its ability to allow the production of better predictive performance compared to a single model. In this context, [PERSON] et al.