Adversarial Attack Strategies on Graph-Based NIDS and Their Latency Impacts Across Datasets

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How do different adversarial attack strategies on graph structure affect the inference latency of GNN-based NIDS models when evaluated using the UNSW-NB15 dataset compared to models trained on the. Deep neural networks, while generalize well, are known to be sensitive to small adversarial perturbations. This phenomenon poses severe security threat and calls for in-depth investigation of the robustness of deep learning models. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do different adversarial attack strategies on graph structure affect the inference latency of GNN-based NIDS models when evaluated using the UNSW-NB15 dataset compared to models trained on the CIC-IDS 2017 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