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.
