Adversarial Deep Learning in Cybersecurity

Name: Adversarial Deep Learning in Cybersecurity
Brand: Springer, Berlin
SKU: 9783030997717
Price: 178.98 EUR
Availability: InStock

Attack Taxonomies, Defence Mechanisms, and Learning Theories

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

Език:

Корици: С твърди корици

Издател: Springer, Berlin

Наличност: Външен склад

Изпращаме след 10-13 дни

178.98 € 350.06 лв

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

Информация за книгата

Автор

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

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2023

страници

300

EAN

9783030997717

Enbook ID

38809270

Издател

Springer, Berlin

Теглоt

630

Размери

155 x 235

Пълно описание

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

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Пълно описание

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