Noise Robust One-Class Intrusion Detection on Dynamic Graphs

TL;DR

This paper presents a probabilistic extension of a graph-based intrusion detection model that significantly improves robustness and detection accuracy in noisy network data environments.

Contribution

It introduces a Gaussian distribution prediction mechanism into TGN-SVDD, enhancing noise robustness in network intrusion detection.

Findings

Improved detection accuracy under noisy conditions

Significant performance gains over baseline models

Robustness increases with higher noise levels

Abstract

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.