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

12/22/2021
by   Christian Toemmel, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/14/2022

Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential Solutions for Fake-Normal Attacks: A Short Review

Smart grid systems are critical to the power industry, however their sop...
research
07/24/2020

Artificial Intelligence in the Creative Industries: A Review

This paper reviews the current state of the art in Artificial Intelligen...
research
05/01/2021

Stabilization of generative adversarial networks via noisy scale-space

Generative adversarial networks (GAN) is a framework for generating fake...
research
08/05/2023

Generative Adversarial Networks for Stain Normalisation in Histopathology

The rapid growth of digital pathology in recent years has provided an id...
research
11/13/2021

Introducing Variational Autoencoders to High School Students

Generative Artificial Intelligence (AI) models are a compelling way to i...
research
04/07/2023

Leveraging GANs for data scarcity of COVID-19: Beyond the hype

Artificial Intelligence (AI)-based models can help in diagnosing COVID-1...
research
05/16/2023

Exploring outlooks towards generative AI-based assistive technologies for people with Autism

The last few years have significantly increased global interest in gener...

Please sign up or login with your details

Forgot password? Click here to reset