Książka Adversarial Deep Learning in Cybersecurity Aneesh Sreevallabh Chivukula

Adversarial Deep Learning in Cybersecurity

Attack Taxonomies, Defence Mechanisms, and Learning Theories

Język: Angielski
Oprawa: Twarda
Wydawca: Springer, Berlin
Dostępność: Dostępna u dostawcy
Wysyłamy za 10-18 dni
726.82
Existing adversarial learning algorithms differ in design assumptions regarding adversary's knowledg...

Informacje o książce

Język
Angielski
Oprawa
Książka - Twarda
Data wydania
2023
strony
300
EAN
9783030997717
Enbook ID
38809270
Waga
630
Wymiary
155 x 235

Pełny opis

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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