Synthetic Network Data Generation for Analyst Training

Abstract

Rapidly evolving cyber threats demand continuous, high-fidelity training for defense analysts. However, generating realistic network traffic datasets creates a significant barrier to entry, often requiring extensive virtualization infrastructure, specialized hardware, and knowledge in cyber range administration. This paper introduces a streamlined architecture, called Generative Packet Captures (GenCap), built upon the foundational capabilities of the FOSR benign traffic generator and the ID2T attack injector. By abstracting these complex tools behind an automated orchestration layer, it enables users to generate scenario-specific PCAP files on demand. This approach democratizes access to training data, allowing analysts to create rigorous network defense scenarios without the need for complex provisioning or systems engineering knowledge.