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.
@article{arxiv.2508.14192,
title = {Noise Robust One-Class Intrusion Detection on Dynamic Graphs},
author = {Aleksei Liuliakov and Alexander Schulz and Luca Hermes and Barbara Hammer},
journal= {arXiv preprint arXiv:2508.14192},
year = {2025}
}