Oprea, A.
, Singhal, A.
and Vassilev, A.
(2022),
Poisoning Attacks against Machine Learning: Can Machine Learning be Trustworthy?, Computer (IEEE Computer), [online], https://doi.org/10.1109/MC.2022.3190787, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934932
(Accessed December 3, 2024)
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Poisoning Attacks against Machine Learning: Can Machine Learning be Trustworthy?
Published
Author(s)
Alina Oprea, ,
Abstract
Many practical applications benefit from Machine Learning (ML) and Artificial Intelligence (AI) technologies, but their security needs to be studied in more depth before the methods and algorithms are actually deployed in critical settings. In this article, we discuss the risk of poisoning attacks when training machine learning models and discuss challenges for defending against this threat.Citation
Computer (IEEE Computer)
Volume
55
Issue
11
Pub Type
Journals
Download Paper
Keywords
artificial intelligence technologies, machine learning trustworthiness, poisoning attacks, security
Citation
Issues
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Created October 25, 2022, Updated May 1, 2024