Scaling Efficiency and Robustness Trade-offs in GNN-Based NIDS via Gradient Bypass

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the computational efficiency of bypassing obfuscated gradients in GNN-based NIDS models scale with increasing network size, and what is the trade-off between robustness and inference time on. Machine Learning has been steadily gaining traction for its use in Anomaly-based Network Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed using the KDDCUP99 dataset as a benchmark. 17 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.1/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the computational efficiency of bypassing obfuscated gradients in GNN-based NIDS models scale with increasing network size, and what is the trade-off between robustness and inference time on the KDD Cup 99 dataset?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance