Artificial Intelligence: Adversarial Machine Learning

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The NCCoE has released the draft NISTIR 8269, A Taxonomy and Terminology of Adversarial Machine Learning. Use the buttons below to view this publication and to submit comments. 

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Current Status

We are seeking your feedback on the draft NISTIR 8269, A Taxonomy and Terminology of Adversarial Machine Learning. The public comment period for this report will close on December 16, 2019.

If you have questions or suggestions, please email us at ai-nccoe@nist.gov

Summary

Machine Learning (ML) is a field within Artificial Intelligence (AI) that focuses on the ability of computers to learn on their own without being programmed. When these systems are misled or given malicious inputs, Adversarial Machine Learning (AML) has likely been employed. AML is conducted by malicious actors to undermine the capabilities of ML; whereas, ML security focuses on understanding these attack consequences with the intention to mitigate the effects of malicious actors.

NISTIR 8269, A Taxonomy and Terminology of Adversarial Machine Learning was developed as a step toward securing applications of AI, specifically AML, and features a taxonomy of concepts and terminologies. This NISTIR can inform future standards and best practices for assessing and managing ML security by establishing a common language and understanding of the rapidly developing AML landscape.

If you have any questions or suggestions, please email the project team at ai-nccoe@nist.gov.