#evasion-attacks
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Adversarial Training Best Practices That Survive Evaluation
A practitioner's guide to adversarial training: pick a threat model, run PGD-AT correctly, catch robust overfitting, and evaluate with AutoAttack.
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PLAA: A 92.78% NIDS Evasion Rate and Feature-Space Attacks
PLAA builds adversarial network traffic at the packet level instead of the flow level, reaching a 92.78% evasion rate against deep-learning NIDS.
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Adversarial Examples vs. Data Poisoning: Timing Is Everything
Adversarial examples attack a deployed model at inference; data poisoning attacks the model before it is deployed. Different timing, different defenses.
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Evasion Attacks on Image Classifiers: FGSM, PGD, and C&W
The three foundational gradient-based evasion attacks, what each one actually optimizes, and what the benchmark numbers mean when you're evaluating a defense.