Téléchargez le livre : Machine Learning under Malware Attack
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Description
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. 
Pages
116 pages
Collection
n.c
Parution
2023-01-31
Marque
Springer Vieweg
EAN papier
9783658404413
EAN PDF
9783658404420
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
11
Taille du fichier
2017 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
89,66 €
EAN EPUB
9783658404420
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
11
Taille du fichier
3594 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
89,66 €
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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