# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 2 Related Work  
In recent times, researchers have been attempting to apply statistical methods for DL traffic classification. These methods utilize payload-independent parameters such as packet length, inter-arrival time, and flow duration. This section will analyze and discuss previous work based on DL techniques in SDN for traffic classification. We have summarized the outcomes of these studies in Table 1.  
The first study mentioned in Table 1 is by [PERSON] et al. (2018). They introduced an approach called SDN-HGW, which enhances the management of distributed smart home networks. The approach supports the core network controller and extends control to the access network, allowing for more effective end-to-end network management. This framework is of great importance in smart home networks. It enables awareness of distributed applications using data traffic classification within the network. To achieve this, the authors developed encrypted classifiers called DataNets. These classifiers are designed using three deep learning algorithms: Multilayer Perceptron (MLP), Stacked Autoencoder (SAE), and Convolutional Neural Networks (CNNs). The classifiers are trained using an open dataset that includes data from 15 applications, encompassing over 200,000 encrypted data samples. The study's results demonstrate the applicability of these classifiers in a smart home SDN environment, enabling distributed application awareness. [PERSON] et al. (2019) introduced a novel classification approach, as outlined in their research. This approach relies on a hybrid deep neural network for application classification. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref.** & **Classification Objective** & **Model Input** & **Model Output** & **Accuracy** \\ \hline
[18] & QoS-aware & Automatic by algorithm & 15 applications & MLP: 97.14\% \\  & & & SAE: 99.14\% \\  & & & CNN: 99.30\% \\
[19] & QoS-aware & Automatic by algorithm & 10 classes & SAE: \textgreater{}90.00\% \\
[20] & Application-aware & Automatic by algorithm & 8 applications & ML-LSTM: 97.14\% \\  & & & CNN: 93.35\% \\
[21] & Application-aware & 4 features & 6 applications & MLP: 93.21\% \\  & & & SAE: 93.13\% \\
[22] & QoS-aware & 23 features & 3 classes & CNN: 96.00\% \\  & & & DNN: 94.00\% \\
[23] & Application-aware & Flow features selected & CNN: autoencoder: \\  & & & & 97.42\% \\  & & & CNN: 96.03\% \\  & & & DNN: 94.36\% \\
[24] & Application-aware & Automatic by algorithm & 15 applications & GAN: 93.18\% \\  & & & CNN: 93.30\% \\
[25] & Application-aware & Automatic by algorithm & 5 classes & CNN: 98.85\% \\  & & & LSTM: 99.22\% \\  & & & SAE: 98.74\% \\
[26] & QoS-aware & Automatic by algorithm & 20 applications & MLP, CNN and SAE: \textgreater{}90.00\% \\
[27] & QoS-aware & 20 features & 8 classes & ResNet: 97.24\% \\
[28] & QoS-aware & 6 features & 6 classes & VAE: 89.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deep learning-based traffic classification in SDN. Notably, it automatically derives flow features from the Stacked Autoencoder (SAE) algorithm, eliminating the need for manual feature selection. In conjunction with this, a centralized SDN controller efficiently acquires and processes extensive network traffic, leveraging its substantial computing capabilities. The findings from their study indicate that this novel classification method outperforms the traditional Support Vector Machine (SVM)-based approach in terms of accuracy. [PERSON] et al. [20] presented a schema for traffic classification in SDN using DL models. They prepared a dataset by preprocessing network traffic flows. They developed two DL classifiers: the Multi-layer Long Short-term Memory (ML-LSTM) model and a combination of a CNN and single-layer LSTM. The hyperparameters of the models were adjusted using a fitting procedure. As indicated by the F1 score, performance analysis revealed the ML-LSTM model's superiority in network packet classification. [PERSON] et al.