# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 2 Related Work
[21] introduced a novel application-aware traffic classification model within an SDN testbed for online and offline usage. This model is integrated into the SDN controller and uses three DL algorithms: MPL, CNN, and SAE. An open dataset of samples from the seven most popular applications was used to train the model. The results showed that the model achieved an impressive accuracy of over 93.00% during offline training and maintained a notable 87.00% accuracy during online testing. [PERSON] and [PERSON] [22] have developed an engineering system that manages traffic in software-defined networking (SDN) to enhance bandwidth allocation for various applications. Their system is based on a classifier built using deep neural network (DNN) and 1D-CNN algorithms. The Synthetic Minority Over-Sampling Technique (SMOTE) addresses imbalanced class distribution within the dataset. The main goal of this system is to improve service quality by assigning different priority queues based on the classification of traffic flows that originate from other applications. Their research shows that DNN and 1D CNN algorithms offer higher accuracy when dealing with traffic data captured within 5 s and 10 s timeouts. [PERSON] et al. [23] introduced a system known as the Convolutional Autoencoder Packet Classifier (CAPC), designed to promptly categorize incoming packets in both fine-grained and coarse-grained manners, distinguishing individual applications and broader categories, respectively, using DL. CAPC is a packet-based deep learning model comprising a 1D convolutional neural network and an autoencoder, enabling it to effectively manage dynamic ports, encrypted traffic, and even group similar applications. The classifier is evaluated using a privately collected traffic dataset and a publicly available VPN dataset, highlighting its exceptional performance. CAPC exhibits an accuracy exceeding 97.00% when classifying service traffic types on the public dataset containing 24 services. [PERSON] et al. [24] developed a semi-supervised traffic classification technique in SDN called ByteS-GAN, which utilizes Generative Adversarial Networks (GAN). ByteS-GAN was created to achieve high accuracy, even with limited labeled samples and many unlabeled data. The approach involved modifying the structure and loss function of the traditional GAN discriminator network to enable fine-grained traffic classification. The research results show that ByteS-GAN significantly improves the classifier's performance, outperforming supervised methods such as CNN. In their work, [PERSON] et al. [25] have presented a framework that uses a deep learning algorithm to classify encrypted network traffic. The framework comprises three modules, with the first module responsible for preprocessing the network flows, the second module focusing on training the classifier, and the third module dealing with testing the CNN model. According to their findings, this encrypted network traffic classification framework performs effectively and uses low resources. [PERSON] et al. [26] introduced a framework to enhance the security of the SDN controller while improving the management of smart home networks. This framework, named SDN Home GateWay for Congestion (SDNHGC), is based on DL algorithms. SDNHGC conducts real-time traffic analysis to facilitate the regulation of network capacity and resource allocation. The classifier within the framework was trained and tested using samplesfrom 20 applications sourced from an open database, employing algorithms such as SAE, MLP, and CNN. The results demonstrate that this approach achieves a level of accuracy superior to other existing solutions. [PERSON] et al. [27] sought to tackle the challenge of interpretability in DL-based traffic classification by introducing an explanatory method incorporating a genetic algorithm. They designed a traffic classifier based on the ResNet model and employed the genetic algorithm to create optimal feature selection masks. This innovative approach yielded an impressive accuracy rate of around 97.24%.