A new adversarial generator for Linux ELF malware achieves 67.74% evasion against MalConv by inserting benign-like strings, with the detector showing mean confidence drop of 0.50.
arXiv preprint arXiv:1802.04528 (2018)
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Similarity-constrained adversarial perturbations reduce drift signals in malware classifiers while achieving evasion, with l2 regularization performing best.
citing papers explorer
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Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations
A new adversarial generator for Linux ELF malware achieves 67.74% evasion against MalConv by inserting benign-like strings, with the detector showing mean confidence drop of 0.50.
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Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
Similarity-constrained adversarial perturbations reduce drift signals in malware classifiers while achieving evasion, with l2 regularization performing best.