# SDN Anomalous Traffic Detection Based on Temporal Convolutional Network
## 2 Related Work
### Deep Learning Based Anomaly Traffic Detection Methods  
In the field of anomalous traffic detection, many researchers have widely adopted a variety of deep learning models to improve detection effectiveness. CNN-based methods can effectively identify local patterns and structural features in traffic by mapping network traffic data to a multidimensional space and using convolutional layers to extract spatial features of the data. However, CNNs have certain limitations in processing time series data and are difficult to capture long-term dependencies in the data. RNNs and their variants (e.g., LSTM, GRU), on the other hand, are specialized in processing time series data and are able to memorize previous information and use it for current prediction, thus capturing dynamic changes in time series. However, RNN is prone to the problem of gradient vanishing or gradient explosion when dealing with long sequences, resulting in models that are difficult and slow to train. In addition, some researchers and scholars have proposed methods such as AE networks [24], deep belief networks (DBN) [25], and generative adversarial networks (GAN) [26]. In order to overcome the shortcomings of the above single model, some studies have proposed hybrid models, and although better detection results have been achieved, these hybrid models still have some complexity in feature extraction and model training, and the convergence speed and generalization ability of the model still need to be further improved. [PERSON] et al. [27] proposed a hybrid method based on CNN and LSTM, which firstly structured the detection data, then extracted spatiotemporal features through CNN and LSTM, and finally used Softmax to complete the detection. [PERSON] et al. [28] proposed a two-branch feature extraction network, which uses CNN and RNN, respectively, to extract spatial and temporal features of the data. The advantage of this method is that it does not need to extract traffic features manually and is able to learn complex patterns in the data automatically. By combining the dual advantages of CNN and RNN, the method is not only able to capture the spatial features of the data but also can effectively handle time series data; however, there is a problem of poor model generalization. [PERSON] et al. [29] proposed to use bidirectional LSTM to learn data features, determine anomalies through a classifier, and deploy idle edge nodes in the Internet of Things to increase the flexibility of detection. To overcome the limitations of single-dimensional feature extraction in dealing with complex cyber attack scenarios, [PERSON] et al. [30] proposed a detection method based on multilevel feature fusion. The method employs a bidirectional long short-term memory network (BiLSTM) and a CNN, which are used to extract spatial, temporal, and byte features of the traffic data, respectively, and further fuses these multidimensional features. With this fusion strategy, the method is able to capture the intrinsic characteristics of network traffic more comprehensively, thus realizing more accurate anomaly detection in complex network environments. [PERSON] et al. [31] proposed a HTTPS traffic detection based on the Bidirectional Gating Recurrent Unit (BiGRU) and the Attention Mechanism method. The method extracts the forward and backward features of byte sequences in a session through BiGRU, which in turn captures the temporal dependency of the data. In addition, the introduction of the attention mechanism enables the model to assign different weights to different features, thus enhancing the model's focus on key features. However, the method is highly sensitive to hyper-parameters, which not only increases the complexity of hyper-parameter setting but also requires a large number of experiments to determine the optimal parameter combinations, thus restricting the model's practicability and generalization to a certain extent. [PERSON] et al. [32] proposed a model based on Recombination Generative Adversarial Network (RGAN).