Ensemble Defenses with Adversarial Fine-Tuning for Robust Malware Detection

This report synthesises findings from 13 peer-reviewed papers addressing the following research question: Can ensemble defenses with adversarial fine-tuning improve the accuracy of malware detection models against evasion attacks in large-scale synthetic datasets. 11 claims were extracted from source literature; 11 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: Can ensemble defenses with adversarial fine-tuning improve the accuracy of malware detection models against evasion attacks in large-scale synthetic datasets?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.