Edge weight concentration overcomes node degree blindness in graph based network intrusion detection

Graph-based network intrusion detection almost universally represents attacker behavior through node-centric structural features – degree, centrality, embeddings from graph neural networks – which implicitly assume that an attacker's identity is visible as a distinct node in the host-communication graph. We show that this assumption breaks under a common, realistic condition: network address translation (NAT) or perimeter aggregation, which collapses many real hosts onto a small number of gateway addresses. Using real, labeled flow data from the CICIDS2017 benchmark (Friday afternoon distributed denial-of-service capture, 225,745 flows), we construct time-windowed directed host-communication graphs and show that, at the observed collapse ratio, node-degree features are statistically indistinguishable from benign traffic (Mann-Whitney p = 0.12–0.83 across four window sizes, which are nested aggregations of one underlying one-minute-resolution timestamp field rather than independent replications) while edge-weight concentration separates attack from benign windows with p ranging from 3.9×10⁻¹² at 60 seconds to 0.017 at 10 minutes (n = 3 attack windows at that resolution). Rather than inferring causation from one fixed collapse ratio, we directly test it with a controlled semi-synthetic sweep that redistributes the real attack's flow volume across a varying number k of synthetic source identities: node-degree separation rises monotonically from chance (AUC = 0.61) to perfect (AUC = 1.00) as k grows from 1 to 128, while edge-weight separation is perfect only near full collapse (k ≤ 2) and falls to near-chance once attacker identity becomes even partially visible (k ≥ 4) – direct evidence that the two feature families are complementary along the collapse axis rather than one uniformly dominating the other. At the flow level, a compact graph-context feature set – with a near-tautological heaviest-edge indicator removed and evaluated under window-grouped cross-validation to prevent information leakage between flows sharing a time window – reaches F1 = 0.981 at 9 dimensions against a 78-dimensional CICFlowMeter baseline's F1 = 0.998, at roughly one-tenth the training cost. We report this as a single-attack-type case study in which the identity-collapse mechanism is now measured directly rather than asserted, and we state explicitly what further validation against other attack archetypes would require.