Domain-specific macOS features enable an ML detector to reach 98.5% accuracy on 41k samples and 99.5% on 9k fresh samples, beating prior methods by 16-50%.
IEEE Trans
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper releases two adversarial malware datasets (44k family-labelled, 33k type-labelled) with high evasion rates and demonstrates that 0.5% poisoning injection raises evasion from 26.1% to 92.8%.
citing papers explorer
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The Role of Domain-Specific Features in Malware Detection: A macOS Case Study
Domain-specific macOS features enable an ML detector to reach 98.5% accuracy on 41k samples and 99.5% on 9k fresh samples, beating prior methods by 16-50%.
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Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
The paper releases two adversarial malware datasets (44k family-labelled, 33k type-labelled) with high evasion rates and demonstrates that 0.5% poisoning injection raises evasion from 26.1% to 92.8%.