# Catch Me If You GAN: Using Artificial Intelligence for Fake Log Generation

arXiv: https://arxiv.org/abs/2112.12006 · HTML: https://mlsanity.com/paper/2112.12006/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Deep Learning for Deepfakes Creation and Detection: A Survey](https://mlsanity.com/paper/1909.11573/) (2019)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks](https://mlsanity.com/paper/1702.07983/) (2017)
- [PassGAN: A Deep Learning Approach for Password Guessing](https://mlsanity.com/paper/1709.00440/) (2017)
- [Deepfake detection: humans vs. machines](https://mlsanity.com/paper/2009.03155/) (2020)
- [CoT: Cooperative Training for Generative Modeling of Discrete Data](https://mlsanity.com/paper/1804.03782/) (2018)
- [Investigating the Ecosystem of Offensive Information Security Tools](https://mlsanity.com/paper/2012.08811/) (2020)

## Built on by (most cited first, 2 of 2)

- [SoK: On the Offensive Potential of AI](https://mlsanity.com/paper/2412.18442/) (2024)
- [TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models](https://mlsanity.com/paper/2607.12104/) (2026)

## Abstract (arXiv)

With artificial intelligence (AI) becoming relevant in various parts of everyday life, other technologies are already widely influenced by the new way of handling large amounts of data. Although widespread already, AI has had only punctual influences on the cybersecurity field specifically. Many techniques and technologies used by cybersecurity experts function through manual labor and barely draw on automation, e.g., logs are often reviewed manually by system admins for potentially malicious keywords. This work evaluates the use of a special type of AI called generative adversarial networks (GANs) for log generation. More precisely, three different generative adversarial networks, SeqGAN, MaliGAN, and CoT, are reviewed in this research regarding their performance, focusing on generating new logs as a means of deceiving system admins for red teams. Although static generators for fake logs have been around for a while, their produces are usually easy to reveal as such. Using AI as an approach to this problem has not been widely researched. Identified challenges consist of formatting, dates and times, and overall consistency. Summing up the results, GANs seem not to be a good fit for generating fake logs. Their capability to detect fake logs, however, might be of use in practical scenarios.