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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 ba...
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| Main Authors: | |
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| Group Author: | ; ; |
| Published: |
CRC Press, Taylor & Francis Group,
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| Publisher Address: | Boca Raton, FL : |
| Publication Dates: | 2023. |
| Literature type: | Book |
| Language: | English |
| Edition: | First edition. |
| Subjects: | |
| Summary: |
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. |
| Item Description: | "A Chapman & Hall book." |
| Carrier Form: | xv, 307 pages : illustrations ; 24 cm |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9781032359885 1032359889 9781032359892 1032359897 |
| Index Number: | QA76 |
| CLC: | TP393.08 |
| Call Number: | TP393.08/A135 |
| Contents: | 1. Introduction to Graph Intelligence2. Fundamentals of Graph Representations3. Graph Embedding: Methods, Taxonomies, and Applications4. Toward Graph Neural Networks: Essentials and Pillars5. Graph Convolution Networks: A Journey from Start to End6. Graph Attention Networks: A Journey from Start to End7. Recurrent Graph Neural Networks: A Journey from Start to End8. Graph Autoencoders: A Journey from Start to End9. Interpretable Graph Intelligence: A Journey from Black to White Box10. Toward Privacy Preserved Graph Intelligence: Concepts, Methods, and Applications |