Controllable Network Data Balancing With GANs

The scarcity of network traffic datasets has become a major impediment to recent traffic analysis research. Data collection is often hampered by privacy concerns, leaving researchers with no choice but to capture limited amounts of highly unbalanced network traffic. Furthermore, traffic classes, particularly network attacks, represent the minority making many techniques such as Deep Learning prone to failure. We address this issue by proposing a Generative Adversarial Network for balancing minority classes and generating highly customizable attack traffic. The framework regulates the generation process with conditional input vectors by creating flows that inherit similar characteristics from the original classes while preserving the flexibility to change their properties. We validate the generated samples with four tests. Our results show that the artificially augmented data is indeed similar to the original set and that the customization mechanism aids in the generation of personalized attack samples while remaining close to the original feature distribution.