Custom cover image
Custom cover image

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)

By: Contributor(s): Resource type: Ressourcentyp: BuchBookLanguage: English Publisher: Cambridge ; New York, NY : Cambridge University Press, 2019Description: xii, 325 Seiten : IllustrationenISBN:
  • 9781107043466
Subject(s): Additional physical formats: 9781107338548 | Erscheint auch als: Adversarial machine learning. Online-Ausgabe Cambridge : Cambridge University Press, 2019. 1 Online-RessourceMSC: MSC: 68T05 | 68-02 | *68-02LOC classification:
  • Q325.5
DOI: DOI: 10.1017/9781107338548Summary: "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"--Call number: Grundsignatur: 2019 E 98PPN: PPN: 1631826166
Holdings
Item type Home library Collection Shelving location Call number Status Barcode
Freihandbestand ausleihbar Bibliothek Campus Süd inf 6.21 Lesesaal Wirtschaftswissenschaften und Informatik (LSW) 2019 E 98 Available 53461442090
Total holds: 0
KIT – The University in the Helmholtz Association                         Home | Impressum | Datenschutz | Barrierefreiheit | KIT