Adversarial machine learning:
"Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inferen...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore
Cambridge University Press
2019
|
| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 UER01 UPA01 Volltext |
| Zusammenfassung: | "Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race"... |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 1 Online-Ressource Illustrationen, Diagramme |
| ISBN: | 9781107338548 |
| DOI: | 10.1017/9781107338548 |
Internformat
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| 700 | 1 | |a Tygar, J.D. |e Verfasser |0 (DE-588)1188485938 |4 aut | |
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Datensatz im Suchindex
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| author | Joseph, Anthony D. Nelson, Blaine Rubinstein, Benjamin I. P. Tygar, J.D |
| author_GND | (DE-588)1188483501 (DE-588)1188485997 (DE-588)1188485393 (DE-588)1188485938 |
| author_facet | Joseph, Anthony D. Nelson, Blaine Rubinstein, Benjamin I. P. Tygar, J.D |
| author_role | aut aut aut aut |
| author_sort | Joseph, Anthony D. |
| author_variant | a d j ad adj b n bn b i p r bip bipr j t jt |
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| dewey-full | 006.3/1 |
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| dewey-ones | 006 - Special computer methods |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1017/9781107338548 |
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| id | DE-604.BV045921004 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:30:22Z |
| institution | BVB |
| isbn | 9781107338548 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031303492 |
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| physical | 1 Online-Ressource Illustrationen, Diagramme |
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| publishDate | 2019 |
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| publisher | Cambridge University Press |
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| spelling | Joseph, Anthony D. Verfasser (DE-588)1188483501 aut Adversarial machine learning Anthony D. Joseph, University of California, Berkeley; Blaine Nelson, Google; Benjamin I.P. Rubinstein, University of Melbourne; J.D. Tygar, University of California, Berkeley Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore Cambridge University Press 2019 1 Online-Ressource Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index "Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race"... COMPUTERS / Security / General / bisacsh Machine learning Computer security COMPUTERS / Security / General Computersicherheit (DE-588)4274324-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Computersicherheit (DE-588)4274324-2 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Nelson, Blaine Verfasser (DE-588)1188485997 aut Rubinstein, Benjamin I. P. Verfasser (DE-588)1188485393 aut Tygar, J.D. Verfasser (DE-588)1188485938 aut Erscheint auch als Druck-Ausgabe, Hardcover 978-1-107-04346-6 http://doi.org/10.1017/9781107338548 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Joseph, Anthony D. Nelson, Blaine Rubinstein, Benjamin I. P. Tygar, J.D Adversarial machine learning COMPUTERS / Security / General / bisacsh Machine learning Computer security COMPUTERS / Security / General Computersicherheit (DE-588)4274324-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4274324-2 (DE-588)4193754-5 |
| title | Adversarial machine learning |
| title_auth | Adversarial machine learning |
| title_exact_search | Adversarial machine learning |
| title_full | Adversarial machine learning Anthony D. Joseph, University of California, Berkeley; Blaine Nelson, Google; Benjamin I.P. Rubinstein, University of Melbourne; J.D. Tygar, University of California, Berkeley |
| title_fullStr | Adversarial machine learning Anthony D. Joseph, University of California, Berkeley; Blaine Nelson, Google; Benjamin I.P. Rubinstein, University of Melbourne; J.D. Tygar, University of California, Berkeley |
| title_full_unstemmed | Adversarial machine learning Anthony D. Joseph, University of California, Berkeley; Blaine Nelson, Google; Benjamin I.P. Rubinstein, University of Melbourne; J.D. Tygar, University of California, Berkeley |
| title_short | Adversarial machine learning |
| title_sort | adversarial machine learning |
| topic | COMPUTERS / Security / General / bisacsh Machine learning Computer security COMPUTERS / Security / General Computersicherheit (DE-588)4274324-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | COMPUTERS / Security / General / bisacsh Machine learning Computer security COMPUTERS / Security / General Computersicherheit Maschinelles Lernen |
| url | http://doi.org/10.1017/9781107338548 |
| work_keys_str_mv | AT josephanthonyd adversarialmachinelearning AT nelsonblaine adversarialmachinelearning AT rubinsteinbenjaminip adversarialmachinelearning AT tygarjd adversarialmachinelearning |