Responsible Graph Neural Networks

This book offers a complete study in the area of graph learning in cyber, emphasising graph neural networks (GNNs) and their cyber security applications.

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.

Autorentext

Mohamed Abdel-Basset is an Associate Professor at the Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt.

Nour Moustafa currently is a Senior Lecturer and Leader of Intelligent Security Group at the School of Engineering and Information Technology, University of New South Wales (UNSW), Canberra, Australia. He is also a Strategic Advisor (AI-SME) at DXC Technology, Canberra.

Hossam Hawash is a Senior Researcher at the Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt.

Zahir Tari is the Research Director of the RMIT Centre of Cyber Security Research and Innovation (CCSRI), Royal Melbourne Institute of Technology, School of Computing Technologies, Melbourne, Australia.

Inhalt

1. Introduction to Graph Intelligence. 2. Fundamentals of Graph Representations. 3. Graph Embedding: Methods, Taxonomies, and Applications. 4. Toward Graph Neural Networks: Essentials and Pillars. 5. Graph Convolution Networks: A Journey from Start to End. 6. Graph Attention Networks: A Journey from Start to End. 7. Recurrent Graph Neural Networks: A Journey from Start to End. 8. Graph Autoencoders: A Journey from Start to End. 9. Interpretable Graph Intelligence: A Journey from Black to White Box. 10. Toward Privacy Preserved Graph Intelligence: Concepts, Methods, and Applications