Federated and Centralized Learning for Malware Detection under Obfuscation and Adversarial Attacks

Abstract

Abstract: This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does the detection accuracy of federated learning models compare to centralized deep neural networks when evaluated on the AndroZoo benchmark with varying levels of code obfuscation and. 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. 7 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the detection accuracy of federated learning models compare to centralized deep neural networks when evaluated on the AndroZoo benchmark with varying levels of code obfuscation and adversarial perturbations?

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