Software Engineering Institute

This presentation was given at FloCon 2023, an annual conference that focuses on applying any and all collected data to defend enterprise networks.

We explore the feasibility of using Generative Adversarial Networks (GANs) to automatically learn generative models to generate synthetic packet- and flow header traces for networking tasks (e.g., telemetry, anomaly detection, provisioning). We identify key fidelity, scalability, and privacy challenges and tradeoffs in existing GAN-based approaches. By synthesizing domain-specific insights with recent advances in machine learning and privacy, we identify design choices to tackle these challenges. Building on these insights, we develop an end-to-end framework, NetShare. We evaluate NetShare on six diverse packet header traces and find that: (1) across distributional metrics and traces, it achieves 46% more accuracy than baselines, and (2) it meets users’ requirements of downstream tasks in evaluating accuracy and rank ordering of candidate approaches.

Attendees Will Learn:

• The motivation behind synthetic data generation and sharing
• The state-of-the-art GAN-based models to generate privacy-preserving synthetic header traces
• The potential use cases for the synthetic head traces

For security operations:

• Data owners can now safely share sensitive traces (or security events) without worrying about the privacy leakage
• The security researchers could develop more robust models based on the valuable data shared by the data owners