Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks

arXiv:2403.11830v1 Announce Type: new
Abstract: Machine Learning (ML) algorithms have become increasingly popular for supporting Network Intrusion Detection Systems (NIDS). Nevertheless, extensive research has shown their vulnerability to adversarial attacks, which involve subtle perturbations to the inputs of the models aimed at compromising their performance. Recent proposals have effectively leveraged Graph Neural Networks (GNN) to produce predictions based also on the structural patterns exhibited by intrusions to enhance the detection robustness. However, the adoption of GNN-based NIDS introduces new types …

adversarial adversarial attacks algorithms arxiv attacks cs.ai cs.cr detection graph inputs intrusion intrusion detection machine machine learning network network intrusion networks neural networks nids performance popular problem research space systems vulnerability