VentureBeat

Adversarial attacks in machine learning: What they are and how to stop them

Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a ...
Nature

Adversarial Machine Learning

Adversarial machine learning studies the creation and defence against inputs—known as adversarial examples—that are intentionally perturbed to mislead trained models. Deep networks and other ...
Infosecurity-magazine.com

#InfosecurityEurope: Preparing for Adversarial Machine Learning Attacks

Machine learning (ML) and artificial intelligence (AI) are essential components in modern and effective cybersecurity solutions. However, as the use of ML and AI in cybersecurity is increasingly ...
AFCEA

Adversarial Machine Learning Poses a New Threat to National Security

Imagine the following scenarios: An explosive device, an enemy fighter jet and a group of rebels are misidentified as a cardboard box, an eagle or a sheep herd. A lethal autonomous weapons system ...
Cisco

Cisco Co-Authors Update to the NIST Adversarial Machine Learning Taxonomy

The rapid evolution and enterprise adoption of AI has motivated bad actors to target these systems with greater frequency and sophistication. Many security leaders recognize the importance and urgency ...
BIOENGINEER

Hopf-inspired generative adversarial network augments univariate time series with bifurcation dynamics

A new generative adversarial framework embeds Hopf bifurcation dynamics and Lyapunov-based regularization into its generator, ...
BIOENGINEER

Fuzzy autoencoder improves containment of adversarial malware

A fuzzy autoencoder classified none of 10,000 adversarially generated executable samples as legitimate and reached a reported ...