# Intelligent Traffic Management in Next-Generation Networks
## 4 Research Challenges and Future Directions  
Research works on the use of ML techniques with networks have produced a multitude of novel approaches. The application of ML/DL models to networking brings several use cases and solutions. However, these solutions suffer from some issues and challenges in practice [50; 148]. Many important factors should be considered when designing an ML-based system in an SDN environment. In this context, this section discusses the key challenges and the issues of using ML/DL algorithms for network management in the SDN environment. Then, we present some research opportunities in related areas. **Imbalanced data samples**: Class imbalances are one of the critical problems in traffic classification/intrusion detection. For example, most of the time, the samples of the anomaly are rare, and there is a great imbalance between the amount of several applications' traffic (i.e., Google, Facebook, Skype) compared to other traffic. A two-class dataset is said to be imbalanced if one of the classes (the minority one) is represented by a very small number of instances in comparison to the other (majority) class. The classifier tends to be biased toward the larger groups that have more observations in the training sample [149]. Therefore, ignoring the unequal misclassification risk for different groups may have a significant impact on the practical use of the classification [23]. Several methods have been proposed as solutions to imbalance class problems, such as under-sampling, over-sampling, synthetic minority over-sampling techniques (SMOTE), or using boosting methods [150]. **Identifying DL architecture**: Engineering the right set of features for a given scenario is often key to the success of a machine-learning project and is not an easy-to-solve question. Therefore, one of the challenges of ML is to automate more and more of the feature engineering process. DL is currently the most active topic of ML. It can process raw data and generate relevant features autonomously. These features are automatically extracted from the training data by DL. However, it is computationally demanding, especially if computing resources are limited. DL has many hyperparameters and their number grows exponentially with the depth of the model. Therefore, it requires a lot of tuning, and there is no clear mathematical proof to interpret its architecture. Finding suitable architecture (i.e., number of hidden layers) and identifying optimal hyperparameters (i.e., learning rate, loss function, etc.) are difficult tasks and can influence the model performance. **Available datasets**: Note that the model training performance is highly dependent on the dataset. Therefore, it is one important component for ML/DL models training, as it has a direct impact on the accuracy and applicability of classifiers [86]. Although there are a few recent public datasets available for traffic classification [88; 96], there is no commonly agreed-upon dataset for most traffic-related classification problems. Additionally, choosing the right dataset is difficult because ML models can only identify applications based on what it has known (existing in the training set). ML algorithms, and especially DL models, can further benefit from training data augmentation. This is indeed an opportunity for using ML/DL algorithms to improve QoS, as the networks generate tremendous amounts of data and the intensive growth of applications on the Internet. However, due to privacy and lack of shareable data, there is difficulty in progressing on traffic classification. To solve these issues, distributed learning and, especially, FL have started to attract more and more researchers for network traffic management. Moreover, due to the huge number of applications on the Internet, the dataset cannot contain all of them. Therefore, the amount of available data could still be insufficient to train ML algorithms effectively, especially DL algorithms. For these reasons, many researchers try to use old datasets to test their solutions.