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Responsible Graph Neural Networks

Mohamed Abdel-Basset | Nour Moustafa | Hossam Hawash | Zahir Tari
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A01=Hossam Hawash
A01=Mohamed Abdel-Basset
A01=Nour Moustafa
A01=Zahir Tari
Adjacency Matrix
advanced graph learning techniques
adversarial machine learning
Adversarial Methods
Author_Hossam Hawash
Author_Mohamed Abdel-Basset
Author_Nour Moustafa
Author_Zahir Tari
Bipartite Graph
Category=UBL
Category=UR
Category=UYQN
Closeness Centrality
Code Snippet
Convolutional Layer
Cyber
cyber threat detection
Decoding Module
Deep Learning Models
Dl Model
Edge Mask
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Graph
Graph Aggregation
Graph Embedding
Graph Filtering
Graph Intelligence
Graph Theory
Heterogenous Graph
Id Space
Import Numpy
Input Graph
interpretable artificial intelligence
Karate Club
Katz Centrality
Laplacian Matrix
machine learning security
Neural Networks
Node Features
Nonnegative Matrix Factorization
Privacy Attacks
privacy preserving algorithms
Python Implementation
Random Graph
reinforcement learning models
Security

Product details

  • ISBN 9781032359892
  • Weight: 600g
  • Dimensions: 156 x 234mm
  • Publication Date: 05 Jun 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.
About Hossam Hawash | Mohamed Abdel-Basset | Nour Moustafa | Zahir Tari

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.

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