HoneyGAN Pots: A Deep Learning Approach for Generating Honeypots

TL;DR

This paper introduces HoneyGAN Pots, a novel deep learning method using GANs to generate decoy configurations for honeypots, aiming to improve adaptability and efficiency in cyber defense strategies.

Contribution

The study pioneers the use of GANs for generating honeypot decoy configurations, addressing limitations of existing static and collection-based approaches.

Findings

Demonstrates feasibility of GAN-based decoy generation

Shows improved adaptability over traditional methods

Provides a new tool for cyber defense enhancement

Abstract

This paper investigates the feasibility and effectiveness of employing Generative Adversarial Networks (GANs) for the generation of decoy configurations in the field of cyber defense. The utilization of honeypots has been extensively studied in the past; however, selecting appropriate decoy configurations for a given cyber scenario (and subsequently retrieving/generating them) remain open challenges. Existing approaches often rely on maintaining lists of configurations or storing collections of pre-configured images, lacking adaptability and efficiency. In this pioneering study, we present a novel approach that leverages GANs' learning capabilities to tackle these challenges. To the best of our knowledge, no prior attempts have been made to utilize GANs specifically for generating decoy configurations. Our research aims to address this gap and provide cyber defenders with a powerful tool to bolster their network defenses.