# Intelligent Traffic Management in Next-Generation Networks
## 3 DL/ML Applications in Software-Defined Networking
### Traffic Classification
#### 3.1.1 Existing Solutions
However, they used three heterogeneous classifiers (SVM, Bayes classifier, K-NN) instead of three identical classifiers to increase the difference among classifiers. To verify the performance of their framework, they used a real dataset and trained the classifier with different ratios of unlabeled application flows. However, the authors did not mention information about the feature selection method and the dataset used during the experiments. In addition, [PERSON] et al. [63] proposed a DL-based model in the SDN-based environment to classify the traffic to one of several classes (Bulk, Database, Interactive, Mail, Services, WWW, P2P, Attack, Games, Multimedia). It consists of the stacked autoencoder (SAE) and softmax classifier. SAE was used for feature extraction and softmax was used as a supervised classifier. The experimental results show that the proposed model outperforms the SVM. However, the authors proposed only the framework without testing its performance in the SDN environment. Finally, to avoid data labeling, K-means has been proposed by [PERSON] et al. [87]. In this paper, several supervised learning methods were trained and evaluated individually, including SVM, DT, RF, and KNN. The results demonstrate that SVM has the highest accuracy with 96.37%. For this paper, the \"IP Network Traffic Flows, Labeled with 75 Apps\" dataset was used [88]. However, the features used for the models' training are selected manually;  
* **Fine-grained traffic classification** Fine-grained classification aims to classify the network traffic by application (the exact application generating the traffic) and facilitates the operators to understand users' profiles and hence provide better QoS. [PERSON] et al. [89] presented a mobile application detection framework called Atlas. This framework enables fine-grained application classification. The _netstat_ logs from the employee devices are collected by the agents to collect ground truth data and are sent to the control plane, which runs the decision tree algorithm (C5.0) to identify the application. Their framework is capable of detecting a mobile application with 94% accuracy for the top 40 Android applications. However, the authors did not present the features used in this framework. [PERSON] and [PERSON] [90] presented MultiClassifier, an application classifier, by combining both DPI and ML to do classification in SDN. They try to exploit the advantage of both methods to achieve a high speed with acceptable accuracy. When a new flow arrives, MultiClassifier gives ML a higher priority to classify because it is much faster than DPI. If the reliability of the ML result is larger than a threshold value, its result will be the MultiClassifier results. If not and if DPI does not return \"UNKNOWN\", MultiClassifier will take the DPI result. This combination succeeded in reaching more than 85% accuracy. Based on the experimental results presented in their paper, we can reveal some conclusions: (i) ML runs four times faster than DPI, and (ii) DPI gives high accuracy. However, the authors did not present the features nor the ML algorithm used for the classification. Moreover, the simulations were not scaled, since they used only two hosts, and thus we cannot confirm the quality of this combination. [PERSON] et al. [91] proposed a traffic classification using supervised learning models in the SDN environment. Specifically, a brief comparative analysis of SVM, Naive Bayes, and KNN has been done, where the accuracy of traffic classification is greater than 90% in all the three supervised learning models. However, the authors used only three applications (SMTP, VLC, HTTP), which is therefore not enough to verify the performance of the models. In addition, [PERSON] et al. [62] introduced a traffic classification architecture based on an SDN environment deployed in an enterprise network using ML.