Adversarial Graph Perturbations on Contrastive GNNs and Autoencoders in Intrusion Detection

Abstract

Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: What is the impact of adversarial graph perturbations on the detection accuracy of contrastive graph neural networks versus autoencoder models in intrusion detection tasks, as evaluated on standard. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of adversarial graph perturbations on the detection accuracy of contrastive graph neural networks versus autoencoder models in intrusion detection tasks, as evaluated on standard benchmarks like GIN, GAT, and GCN with metrics such as robustness (accuracy drop ratio) and generalization across different attack types?

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