  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.
