Adversarial Fine-Tuning and Ensemble Defenses in Large-Scale Malware Detection Efficiency

This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does adversarial fine-tuning with ensemble defenses impact the inference efficiency (e.g., latency, throughput) of malware detection models on large-scale synthetic datasets compared to vanilla. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does adversarial fine-tuning with ensemble defenses impact the inference efficiency (e.g., latency, throughput) of malware detection models on large-scale synthetic datasets compared to vanilla models?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.