Responsible Graph Neural Networks Paperback / softback
by Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Zahir Tari
Paperback / softback
- Information
Description
More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists.
This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications. Three parts examine the basics, methods and practices, and advanced topics.
The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications.
The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models.
The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details. Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.
Information
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Out of StockMore expected soonContact us for further information
- Format:Paperback / softback
- Pages:307 pages, 41 Halftones, black and white; 41 Illustrations, black and white
- Publisher:Taylor & Francis Ltd
- Publication Date:05/06/2023
- Category:
- ISBN:9781032359885
Other Formats
- PDF from £34.82
- EPUB from £34.82
- Hardback from £62.40
Information
-
Out of StockMore expected soonContact us for further information
- Format:Paperback / softback
- Pages:307 pages, 41 Halftones, black and white; 41 Illustrations, black and white
- Publisher:Taylor & Francis Ltd
- Publication Date:05/06/2023
- Category:
- ISBN:9781032359885