# Noise Robust One-Class Intrusion Detection on Dynamic Graphs

arXiv: https://arxiv.org/abs/2508.14192 · HTML: https://mlsanity.com/paper/2508.14192/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems](https://mlsanity.com/paper/2207.03576/) (2022)
- [One-Class Intrusion Detection with Dynamic Graphs](https://mlsanity.com/paper/2508.12885/) (2025)

## Abstract (arXiv)

In the domain of network intrusion detection, robustness against contaminated and noisy data inputs remains a critical challenge. This study introduces a probabilistic version of the Temporal Graph Network Support Vector Data Description (TGN-SVDD) model, designed to enhance detection accuracy in the presence of input noise. By predicting parameters of a Gaussian distribution for each network event, our model is able to naturally address noisy adversarials and improve robustness compared to a baseline model. Our experiments on a modified CIC-IDS2017 data set with synthetic noise demonstrate significant improvements in detection performance compared to the baseline TGN-SVDD model, especially as noise levels increase.