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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 ...

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 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 ...

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 ...

Member of the ICMAT, AXA-ICMAT Chair in Adversarial Risk Analysis and Member of the Spanish Royal Academy of Sciences, Instituto de Ciencias Matemáticas (ICMAT-CSIC) David Rios Insua has received ...

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