UAP4MA: Leveraging Multi-Agent Bandits to Generate Universal Adversarial Perturbations for Malware Attribution

Summary

UAP4MA is a decision-based, problem-space, black-box method for generating universal adversarial perturbations against APT malware attribution models. It uses a multi-agent Multi-Armed Bandit framework that applies functionality-preserving transformations from 13 types. On the AMG43 and APTMalware datasets, it achieves over four times the fooling rate, double the attack success rate, and an 80% reduction in training time compared with state-of-the-art methods.

Topics

• Adversarial machine learning

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