LogGenAgent: An Agentic Synthetic Security Log Generation System

What is it about?

Cybersecurity researchers rely heavily on system and security logs to study attacks, develop detection systems, and evaluate security tools. However, obtaining realistic security logs is challenging because real logs often contain sensitive information, are difficult to share, and may not cover a wide variety of attack scenarios. This work introduces LogGenAgent, an AI-powered framework that generates realistic synthetic security logs. The system focuses on multi-host, multi-stage environments where events occur across multiple machines and different phases of an attack. Unlike traditional approaches that generate logs in a single step, LogGenAgent follows an agent-based workflow. It first generates logs using a Large Language Model (LLaMA 3.1) and then evaluates the generated logs using several complementary quality checks. The framework measures semantic similarity, statistical properties, structural consistency, workflow correctness, and distinguishability from real logs. Based on the evaluation results, the system automatically refines its generation strategy and produces improved logs in subsequent iterations. This evaluation-driven refinement process helps the generated logs better match the characteristics of real-world security data. Experiments using authentication and system logs collected from a controlled laboratory environment show that LogGenAgent produces synthetic logs that are closer to real logs than traditional one-shot generation methods. The generated logs preserve important characteristics such as event distributions, workflow patterns, and log templates while remaining difficult to distinguish from real data. The proposed framework can help researchers, educators, and security practitioners obtain realistic cybersecurity datasets without exposing sensitive operational information.

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Photo by Jake Walker on Unsplash

Photo by Jake Walker on Unsplash

Why is it important?

High-quality cybersecurity datasets are essential for developing intrusion detection systems, threat hunting tools, and security analytics platforms. Unfortunately, real security logs are often confidential, difficult to share, and expensive to collect. This creates a major challenge for researchers attempting to reproduce experiments and compare new security techniques. LogGenAgent addresses this problem by providing a systematic approach for generating realistic synthetic security logs. The framework combines large language models with an agentic feedback loop that continuously evaluates and improves generated outputs. Rather than relying solely on text similarity, the system considers statistical behavior, workflow consistency, structural patterns, and machine-learning-based distinguishability tests. The resulting logs are more realistic and better preserve the characteristics of real-world environments. This work contributes toward creating privacy-preserving cybersecurity datasets that can support research, education, benchmarking, and future intrusion detection studies. As AI-generated cybersecurity data becomes increasingly important, LogGenAgent offers a practical and scalable solution for generating realistic multi-host, multi-stage security logs.

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