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• W4225586443 abstract "Recent innovations in machine learning enjoy a remarkable rate of adoption across a broad spectrum of applications, including cyber-security. While previous chapters study the application of machine learning solutions to cyber-security, in this chapter we present adversarial machine learning: a field of study concerned with the security of machine learning algorithms when faced with attackers. Likewise, adversarial machine learning enjoys remarkable interest from the community, with a large body of works that either propose attacks against machine learning algorithms, or defenses against adversarial attacks. In particular, adversarial attacks have been mounted in almost all applications of machine learning. Here, we aim to systematize adversarial machine learning, with a pragmatic focus on common computer security applications. Without assuming a strong background in machine learning, we also introduce the basic building blocks and fundamental properties of adversarial machine learning. This study is therefore accessible both to a security audience without in-depth knowledge of machine learning and to a machine learning audience." @default.
• W4225586443 created "2022-05-05" @default.
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• W4225586443 date "2022-01-01" @default.
• W4225586443 modified "2023-10-16" @default.
• W4225586443 title "Adversarial Machine Learning" @default.
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• W4225586443 doi "https://doi.org/10.1007/978-3-030-98795-4_12" @default.
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