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 ...
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 ...
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 ...
This activity was supported by Contract 2014-14041100003-019 with the Office of the Director of National Intelligence. Any opinions, findings, conclusions, or recommendations expressed in this ...
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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 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 ...
Data poisoning can render machine learning models inaccurate, possibly resulting in poor decisions based on faulty outputs. With no easy fixes available, security pros must focus on prevention and ...
Long gone are the days of only discovering the existence of cyber threats and deciding what to name each of them. Cyberthreats grow—not only in complexity but in frequency, and one of the things that ...
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