PcapGAN: Packet Capture File Generator by Style-Based Generative Adversarial Networks

After the advent of GAN technology, many varied models have been studied and applied to various fields such as image and audio. However, in the field of cyber data, which has the same issue of data shortage, the research on data augmentation is insufficient. To solve this problem, we propose PcapGAN that can augment pcap data, a kind of network data. The proposed model includes an encoder, a data generator, and a decoder. The encoder subdivides network data into four parts. The generator generates new data for each part of the data. The decoder combines the generated data into realistic network data. We demonstrate the similarity between the generated data and original data, and validation of the generated data by increased performance of intrusion detection algorithms.