Presentation of the paper “Robustness Evaluation of Graph Neural Network-based Network Intrusion Detection Systems against Adversarial Flow Injection” at SECRYPT 2026

This paper investigates the robustness of Graph Neural Network-based Network Intrusion Detection Systems (GNN-NIDS) against adversarial flow injection attacks. We propose a novel attack strategy that leverages the structural properties of network traffic graphs to evade detection. Our evaluation demonstrates the effectiveness of the proposed attack and highlights the need for improved defense mechanisms in GNN-NIDS.