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• W4211047985 endingPage "169" @default.
• W4211047985 startingPage "1" @default.
• W4211047985 abstract "The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop." @default.
• W4211047985 created "2022-02-13" @default.
• W4211047985 creator A5038669899 @default.
• W4211047985 creator A5087192873 @default.
• W4211047985 date "2018-08-08" @default.
• W4211047985 modified "2023-10-16" @default.
• W4211047985 title "Adversarial Machine Learning" @default.
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• W4211047985 doi "https://doi.org/10.2200/s00861ed1v01y201806aim039" @default.
• W4211047985 hasPublicationYear "2018" @default.
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