Adversarial Deep Learning in Cybersecurity

Name: Adversarial Deep Learning in Cybersecurity
Brand: Springer, Berlin
SKU: 9783030997717
Price: 3882 CZK
Availability: InStock

Attack Taxonomies, Defence Mechanisms, and Learning Theories

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

Jazyk:

Vazba: Pevná

Vydavatel: Springer, Berlin

Dostupnost: Skladem u dodavatele

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3 882 Kč

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

Informace o knize

Autor

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

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2023

Stránek

300

EAN

9783030997717

Enbook ID

38809270

Vydavatel

Springer, Berlin

Hmotnost

630

Rozměry

155 x 235

Kompletní 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

Informace o knize

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