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Found 4 papers, 1 papers with code
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
Evaluating the adversarial robustness of machine learning models using gradient-based attacks is challenging.
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks
Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks for higher sparsity.
Adversarial Attacks Against Uncertainty Quantification
Machine-learning models can be fooled by adversarial examples, i. e., carefully-crafted input perturbations that force models to output wrong predictions.
Explaining Machine Learning DGA Detectors from DNS Traffic Data
One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of infected devices (botnet) gets exploited to flood the computational capacity of services through the commands of an attacker.
