GNN-Guided Selection of Benign-like Anomalies for Backdoor Attacks Against Network Intrusion Detection Systems

Summary

This paper studies a backdoor attack on AI-based network intrusion detection systems (NIDSs). The method uses the embedding space of a Graph Attention Network (GAT) to pick anomalous samples close to benign traffic, adds a benign-distribution trigger, and relabels them as benign before retraining TabNet, ACID, and AlertNet. Tests on NSL-KDD, CICIDS2017, and UNSW-NB15 show high attack success rates, but the gain over random selection is uneven and marginal or absent where the random baseline is already saturated.

Topics

• Adversarial machine learning

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