Federated Learning Malware Detection Robustness Under Aggregation-Targeted Adversarial Attacks

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How robust are federated learning-based malware detection models to adversarial attacks targeting the aggregation process, measured by the degradation in F1-score when subjected to gradient poisoning. This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated learning to detect malware affecting IoT devices is. 5 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.1/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How robust are federated learning-based malware detection models to adversarial attacks targeting the aggregation process, measured by the degradation in F1-score when subjected to gradient poisoning or model inversion attacks on the N-BaIoT 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