Adversarial Deep Learning in Cybersecurity

Name: Adversarial Deep Learning in Cybersecurity
Brand: Springer, Berlin
SKU: 9783030997717
Price: 169.82 EUR
Availability: InStock

Attack Taxonomies, Defence Mechanisms, and Learning Theories

Autor: Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou

Jazyk:

Väzba: Pevná

Vydavateľ: Springer, Berlin

Dostupnosť: Skladom u dodávateľa

Odosielame za 10-13 dní

169.82 €

Existing adversarial learning algorithms differ in design assumptions regarding adversary's knowledg...

Informácie o knihe

Autor

Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou

Jazyk

Angličtina

Väzba

Kniha - Pevná

Vydalo

2023

Stránok

300

EAN

9783030997717

Enbook ID

38809270

Vydavateľ

Springer, Berlin

Hmotnosť

630

Rozmery

155 x 235

Kompletný popis

Existing adversarial learning algorithms differ in design assumptions regarding adversary's knowledge, attack strategies, attack influence, and security violation. In this book provides insights on the relation between adversarial learning and cybersecurity. The authors survey and summarize non-stationary data representations learnt by deep learning networks in big data, evolutionary computing, fog computing, cyber-physical systems, transfer learning, sparse learning, robust learning, and reinforcement learning. The robustness of deep learning networks is examined to produce a taxonomy of adversarial examples and algorithms. The authors also survey the use of game theory, convex optimization and stochastic optimization in adversarial deep learning formulations.

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Adversarial Deep Learning in Cybersecurity

Attack Taxonomies, Defence Mechanisms, and Learning Theories

Informácie o knihe

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