Synthetic Network Data Generation for Analyst Training
Name
Wright_Synthetic Network Data Generation.pdf
Description
Main Report
Size
244.2 KB
Format
Adobe PDF
Checksum (MD5)
310d74f61773a5070e4438dfc68aac76
Author(s)
Smith, Liam
Wright, Matthew
Date Issued
April 1, 2026
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.
Subjects
PCAP (Packet Capture)
IDS (Intrusion Detection System)
RAG (Retrieval-Augmented Generation)
Cyber Range
Large Language Models (LLMs)
MIT Department
Lincoln Laboratory
Persistent DSpace Link
