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Machine Learning under Malware Attack

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Machine Learning under Malware Attack

Raphael Labaca-Castro

Springer Vieweg
Publication date : 2023-01-31

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Machine learning has become key in supporting decision-making processes across a wide array of applications, ranging from autonomous vehicles to malware detection. However, while highly accurate, these algorithms have been shown to exhibit vulnerabilities, in which they could be deceived to return preferred predictions. Therefore, carefully crafted adversarial objects may impact the trust of machine learning systems compromising the reliability of their predictions, irrespective of the field in which they are deployed. The goal of this book is to improve the understanding of adversarial attacks, particularly in the malware context, and leverage the knowledge to explore defenses against adaptive adversaries. Furthermore, to study systemic weaknesses that can improve the resilience of machine learning models.

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About the book

Author

Raphael Labaca-Castro

Imprint

Springer Vieweg

Collection

n.c

Publication date

2023-01-31

Pages

116 pages

Print ISBN

9783658404413

Language

English

Ebook informations

EAN PDF

9783658404420

Price

£74.50

Copy count 1

Print count 11

File size 2017 Ko

Informations accessibility

EAN EPUB

9783658404420

Price

£74.50

Copy count 1

Print count 11

File size 3594 Ko

Informations accessibility

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About author(s)

Raphael Labaca-Castro

Raphael Labaca-Castro is a computer scientist whose primary interests lie in the nexus between Machine Learning and Computer Security. He holds a PhD in Adversarial Machine Learning and currently leads an ML team in the quantum security field.

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