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Название: Responsible Graph Neural Networks
Автор: Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash
Издательство: CRC Press
Год: 2023
Страниц: 324
Язык: английский
Формат: pdf (true)
Размер: 10.29 MB
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. To get you up and running for the hands-on learning experience, we need to set you up with an environment for running Python, Jupyter notebooks, the relevant libraries, and the code needed to run the book itself. The simplest way to get going will be to install Anaconda Framework. The Python 3.x version is required.
Автор: Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash
Издательство: CRC Press
Год: 2023
Страниц: 324
Язык: английский
Формат: pdf (true)
Размер: 10.29 MB
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. To get you up and running for the hands-on learning experience, we need to set you up with an environment for running Python, Jupyter notebooks, the relevant libraries, and the code needed to run the book itself. The simplest way to get going will be to install Anaconda Framework. The Python 3.x version is required.