Könyv Adversarial Deep Learning in Cybersecurity Aneesh Sreevallabh Chivukula

Adversarial Deep Learning in Cybersecurity

Attack Taxonomies, Defence Mechanisms, and Learning Theories

Nyelv: Angol
Kötés: Kemény kötésű
Elérhetőség: Beszállítói készleten
Küldés 10-13 napon belül
65 601 Ft
Existing adversarial learning algorithms differ in design assumptions regarding adversary's knowledg...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2023
oldal
300
EAN
9783030997717
Enbook ID
38809270
Súly
630
Méretek
155 x 235

Teljes leírás

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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