9783031004520 - adversarial machine learning di vorobeychik, yevgeniy; kantarcioglu, murat (16 risultati)
Lingua: Inglese
Editore: Springer, 2018
Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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, fin…ance, 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.The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research.Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.
Lingua: Inglese
Editore: Springer, 2018
Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Condizione: Neu. Adversarial Machine Learning | Yevgeniy Vorobeychik (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xvii | Englisch | 2018 | Springer | EAN 9783031004520 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juerg…en[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer, 2018
Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 visio…n, 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.The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research.Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings. 172 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2018
Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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, a.
Lingua: Inglese
Editore: Springer, Springer Aug 2018, 2018
Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 11 di 14. Libro 11 di 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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, l…anguage, 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.The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research.Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 172 pp. Englisch.
