Supervised and Unsupervised Federated Models Under Adversarial Poisoning in IoT Networks

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the comparative robustness of supervised versus unsupervised federated models against adversarial poisoning attacks in cross-device IoT network traffic analysis. Abstract The integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity has driven a transformational shift, significantly enhancing the ability to detect, respond to, and mitigate complex cyber threats. Traditional defense mechanisms are. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the comparative robustness of supervised versus unsupervised federated models against adversarial poisoning attacks in cross-device IoT network traffic analysis?

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