# Research on the Simulation Method of HTTP Traffic Based on GAN
## 2 Related Research  
The feasibility of using GANs to generate network traffic data has been recognized [13]. Although GAN-based data generation is a relatively new approach, several methods have shown promising results in different aspects. Here are a few notable examples:  
[PERSON] et al. [14] proposed a data generation method based on Wasserstein GAN with gradient penalty (WGAN-GP) using a dual time update rule. The main contribution of the paper lies in the introduction of a single hidden layer IP2 Vec method for network data processing. This approach leverages techniques from natural language processing, such as Word2 Vec, to transform traditional network data into fixed-length vector representations. IP addresses, destination ports, and transport protocols are treated as words and encoded as one-hot vectors. However, this method has limitations as it can only generate existing data by recombining real data, rather than generating entirely new data. [PERSON] et al. [15] explored the possibility of directly generating packet files using the PAC-GAN model. Unlike [PERSON] et al [14]., [PERSON] focused on simulating individual packets. Although the authors achieved a high accuracy of 0.99 in fitting a particular type of DNS packet, the success rate of fitting HTTP packets was only around 0.7. Moreover, the method is limited to simple packet structures and cannot specify the content of the generated packets. The content of the packets generated by GANs is uncontrollable, which hinders their practical application for effective data communication. [PERSON] et al. [16] proposed a GAN-based method for fitting data at the data flow level. In contrast to the previous methods mentioned, the authors focused on shaping malicious traffic generated by malware into normal network traffic to evade detection. They input malicious traffic into the GAN and dynamically adjusted the output traffic by simulating Facebook chat traffic. The authors also envisioned a scenario where intercepted shaped data is fed back to the GAN for modification. This method achieved good practical results in testing. However, it has clear limitations, such as only simulating traffic from specific apps. If the app is not widely used in certain regions, the method becomes ineffective. Additionally, generating a large amount of Facebook-like data in a network environment may raise suspicions. PcapGAN [17] is a GAN model designed to generate enhanced pcap data. The model consists of an encoder, a data generator, and a decoder. The encoder divides the network data into four parts. The generator's task is to generate data for each segmented part, and the decoder combines these scattered data into a cohesive whole, representing real network data for transmission. The entire pcap file is transformed into a directed graph G by creating an IP flow graph. The time intervals between data packets are converted into a layer sequence, and then processed in a mixed structure. However, this approach also faces challenges in generating data in more complex network scenarios. Although the generated data packets have high quality, they are mainly suitable for analyzing network flow graphs and timestamps. ZipNet-GAN [18] is a GAN architecture that combines a new neural network called ZipNet with super-resolution targeting mobile traffic. It consists of several modules, including a spatiotemporal feature module for extracting mobile traffic features, upsampling blocks, and a core Zipper convolution block, forming a deep ZipNet architecture. The architecture specifically includes a 3D upsampling block as input, followed by a 3D deconvolution layer, three 3D convolution layers, a batch normalization layer, and an activation function layer. Without adding additional parameters, the architecture utilizes a set of additional skip connections to achieve ResNet-like architecture with enhanced performance, accelerating the overall training process and outperforming the convolution-based neural network (SRCNN [19]) in terms of prediction accuracy.