Adversarial machine learning - Simple English Wikipedia, the free encyclopedia

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Adversarial machine learning is about studying attacks on machine learning systems and finding ways to protect them. A survey in May 2020 showed that experts want better security for machine learning in real-world use.^[1]^[2]

Most machine learning methods are made for specific tasks and assume that training and test data come from the same pattern. But in real life, especially in important applications, this is not always true. People might give false data on purpose to trick the system.^[3]

References

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↑ Kianpour, Mazaher; Wen, Shao-Fang (2020). "Timing Attacks on Machine Learning: State of the Art". Intelligent Systems and Applications. Advances in Intelligent Systems and Computing. Vol. 1037. pp. 111–125. doi:10.1007/978-3-030-29516-5_10. ISBN 978-3-030-29515-8. S2CID 201705926.
↑ Siva Kumar, Ram Shankar; Nyström, Magnus; Lambert, John; Marshall, Andrew; Goertzel, Mario; Comissoneru, Andi; Swann, Matt; Xia, Sharon (May 2020). "Adversarial Machine Learning-Industry Perspectives". 2020 IEEE Security and Privacy Workshops (SPW). pp. 69–75. doi:10.1109/SPW50608.2020.00028. ISBN 978-1-7281-9346-5. S2CID 229357721.
↑ Qasemi, Behzad (2025-02-09). "Adversarial Machine Learning: A Comprehensive Review of Cyber Threats and Defensive Strategies". Zenodo.

Other websites

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MITRE ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems
NIST 8269 Draft: A Taxonomy and Terminology of Adversarial Machine Learning
NIPS 2007 Workshop on Machine Learning in Adversarial Environments for Computer Security
AlfaSVMLib Archived 2020-09-24 at the Wayback Machine – Adversarial Label Flip Attacks against Support Vector Machines
Laskov, Pavel; Lippmann, Richard (2010). "Machine learning in adversarial environments". Machine Learning. 81 (2): 115–119. doi:10.1007/s10994-010-5207-6. S2CID 12567278.
Dagstuhl Perspectives Workshop on "Machine Learning Methods for Computer Security"
Workshop on Artificial Intelligence and Security, (AISec) Series

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