Defending Network Intrusion Detection Systems Based on Graph Neural Networks Against Structural Adversarial Attacks

arXiv:2608.24454 · cs.CR · Submitted 2026-08-25 · Read on arXiv

cs.CR

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/dimgalli/defending-gnn-nids

Terminology

• Adversarial Attacks — 31× in this paper · explained in 1 episode
• Adversarial Training — 19× in this paper · explained in 1 episode
• Robustness — 19× in this paper · explained in 5 episodes
• Detection Rate — 9× in this paper
• Machine Learning — 8× in this paper
• GNNs — 7× in this paper · explained in 2 episodes
• Adversarial Examples — 6× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Adversarial Robustness — 5× in this paper · explained in 3 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adversarial Defense — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks
• REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack
• Explaining and Harnessing Adversarial Examples
• Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
• Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
• Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
• Intriguing properties of neural networks
• Towards Deep Learning Models Resistant to Adversarial Attacks
• Bag of Tricks for Adversarial Training
• On the Effectiveness of Adversarial Training on Malware Classifiers

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