Inductive Disagreement-Aware Graph Neural Networks for Real-Time Network Intrusion Detection
Keywords:
network intrusion detection, graph neural networks, inductive learning, anomaly detection, real-time systems, scalable graph learningAbstract
Graph neural networks exploit relational context for intrusion detection, but most flow-level designs induce the graph over the union of labelled and unlabelled records. That choice is transductive: every unseen event must be present before the graph exists, which forbids per-event scoring and leaves an unmeasured optimism in reported accuracy. We present ID-GNN, a strictly inductive disagreement-aware framework. A searchable reference index is built once from labelled rows only and stores, for each of R fixed random projections, a sorted array of composite keys formed from a categorical signature and the monotone rank of the projection. An arriving flow is placed into every ordering with two binary searches, so attachment costs O(R log B) for B reference rows and no edge between two unlabelled events is ever formed. On top of this index we materialise disagreement at two scales: a dispersion-standardised local residual and a categorical-stratum residual. On the official UNSW-NB15 partition the reference views raise a strong gradient-boosted detector from 0.9849 to 0.9870 ROC-AUC and 0.9888 to 0.9903 PR-AUC, while a transductive control of the same framework reaches only 0.9866, showing that the inductive restriction costs nothing. Single-event latency is 0.669 ms at the median and 1.497 ms at p99 with a 149,039-row index; growing the index 15-fold raises median latency by 14%, and index memory is exactly linear (7.7 MB at 25k rows, 154.0 MB at 500k). A 5,000-row reference (1.55 MB) already recovers 43% of the full gain. On NSL-KDD, where the official test partition contains attack families absent from training, the same views lift a compact neural detector from 0.9378 to 0.9646 ROC-AUC and cut seed variance five-fold.
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