# Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2212.14109 · HTML: https://mlsanity.com/paper/2212.14109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Synthesizing Tabular Data using Generative Adversarial Networks](https://mlsanity.com/paper/1811.11264/) (2018)
- [Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN](https://mlsanity.com/paper/1702.05983/) (2017)
- [Voice Impersonation using Generative Adversarial Networks](https://mlsanity.com/paper/1802.06840/) (2018)
- [Evaluation of Machine Learning Algorithms for Intrusion Detection System](https://mlsanity.com/paper/1801.02330/) (2018)

## Abstract (arXiv)

Network Intrusion Detection Systems (NIDS) are tools or software that are widely used to maintain the computer networks and information systems keeping them secure and preventing malicious traffics from penetrating into them, as they flag when somebody is trying to break into the system. Best effort has been set up on these systems, and the results achieved so far are quite satisfying, however, new types of attacks stand out as the technology of attacks keep evolving, one of these attacks are the attacks based on Generative Adversarial Networks (GAN) that can evade machine learning IDS leaving them vulnerable. This project investigates the impact of the Adversarial Attacks synthesized using real DDoS attacks generated using GANs on the IDS. The objective is to discover how will these systems react towards synthesized attacks. marking the vulnerability and weakness points of these systems so we could fix them.