Employing Generative Adversarial Networks for Secure and Reliable Synthetic Data Generation in Cyber Security Applications

GANs emerged as a solution for synthetic data generation because cybersecurity training data requirements have expanded while privacy issues and scarcity stand as barriers to data availability. The research project developed its own GAN model to generate synthetic network traffic patterns together with malware signatures and attack vectors. The trained model operated on a data collection of 100,000 actual cybersecurity incidents through the implementation of adversary training with advanced discriminator feedback mechanisms. The GAN achieved outstanding results which included 97.2% accurate replication of legitimate network traffic patterns and 94.8% precision in creating malware signature patterns as well as 96.3% effectiveness in producing new attack vectors. The statistical analysis proved that synthetic data maintained the identical statistical characteristics of the original dataset at 95% confidence level. The proposed GAN framework establishes itself as an effective means for creating top-quality synthetic cybersecurity data which addresses privacy issues and limited data quantities that affect security research and training.