Sprache: Englisch
Verlag: Morgan & Claypool (edition ), 2018
ISBN 10: 1681733951 ISBN 13: 9781681733951
Anbieter: BooksRun, Philadelphia, PA, USA
Paperback. Zustand: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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In den WarenkorbZustand: New. In English.
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In den WarenkorbZustand: New. pp. 478.
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In den WarenkorbPaperback. Zustand: Brand New. 2013 edition. 268 pages. 9.00x6.00x0.50 inches. In Stock.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the 4th International Conference on Decision and Game Theory for Security, GameSec 2013, held in Fort Worth, TX, USA, in November 2013. The 15 revised full papers presented were carefully reviewed and selected from numerous submissions. The conference focuses on analytical models based on game, information, communication, optimization, decision, and control theories that are applied to diverse security topics. At the same time, the connection between theoretical models and real world security problems are emphasized to establish the important feedback loop between theory and practice. Observing the scarcity of venues for researchers who try to develop a deeper theoretical understanding of the underlying incentive and resource allocation issues in security, we believe that GameSec will fill an important void and serve as a distinguished forum of highest standards for years to come.
Taschenbuch. Zustand: Neu. Decision and Game Theory for Security | 4th International Conference, GameSec 2013, Fort Worth, TX, USA, November 11-12, 2013, Proceedings | Sajal K. Das (u. a.) | Taschenbuch | Lecture Notes in Computer Science | x | Englisch | 2013 | Springer | EAN 9783319027852 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, 2018
ISBN 10: 3031004523 ISBN 13: 9783031004520
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: 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, 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.
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In den WarenkorbZustand: New. Thuraisingham, Bhavani Cadenhead, Tyrone Kantarcioglu, Murat Khadilkar, VaibhavWith an ever-increasing amount of information on the web, it is critical to understand the pedigree, quality, and accuracy of your data. Using provenance, you can a.
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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 478 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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In den WarenkorbGebunden. Zustand: New. Thuraisingham, Bhavani Cadenhead, Tyrone Kantarcioglu, Murat Khadilkar, VaibhavWith an ever-increasing amount of information on the web, it is critical to understand the pedigree, quality, and accuracy of your data. Using provenance, you can a.
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In den WarenkorbHardcover. Zustand: Brand New. 344 pages. 9.19x6.13x1.00 inches. In Stock.