Intrusion detection systems, long constrained by high false-positive rates and limited adaptability, are being re-engineered ...

The integration of deep learning techniques into wireless communication systems has catalysed notable advancements in tasks such as modulation classification and spectrum sensing. However, the ...

AI-driven systems have become prime targets for sophisticated cyberattacks, exposing critical vulnerabilities across industries. As organizations increasingly embed AI and machine learning (ML) into ...

The final guidance for defending against adversarial machine learning offers specific solutions for different attacks, but warns current mitigation is still developing. NIST Cyber Defense The final ...

The Artificial Intelligence and Machine Learning (“AI/ML”) risk environment is in flux. One reason is that regulators are shifting from AI safety to AI innovation approaches, as a recent DataPhiles ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Security leaders’ intentions aren’t matching up with their actions to ...

NIST’s National Cybersecurity Center of Excellence (NCCoE) has released a draft report on machine learning (ML) for public comment. A Taxonomy and Terminology of Adversarial Machine Learning (Draft ...

The National Institute of Standards and Technology (NIST) has published its final report on adversarial machine learning (AML), offering a comprehensive taxonomy and shared terminology to help ...

The Steacie Prize is awarded to a person 40-years-old or younger, who has made notable contributions to research in Canada ...