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10 Machine Learning Blueprints You Should Know for Cybersecurity

You're reading from  10 Machine Learning Blueprints You Should Know for Cybersecurity

Product type Book
Published in May 2023
Publisher Packt
ISBN-13 9781804619476
Pages 330 pages
Edition 1st Edition
Languages
Author (1):
Rajvardhan Oak Rajvardhan Oak
Profile icon Rajvardhan Oak

Table of Contents (15) Chapters

Preface 1. Chapter 1: On Cybersecurity and Machine Learning 2. Chapter 2: Detecting Suspicious Activity 3. Chapter 3: Malware Detection Using Transformers and BERT 4. Chapter 4: Detecting Fake Reviews 5. Chapter 5: Detecting Deepfakes 6. Chapter 6: Detecting Machine-Generated Text 7. Chapter 7: Attributing Authorship and How to Evade It 8. Chapter 8: Detecting Fake News with Graph Neural Networks 9. Chapter 9: Attacking Models with Adversarial Machine Learning 10. Chapter 10: Protecting User Privacy with Differential Privacy 11. Chapter 11: Protecting User Privacy with Federated Machine Learning 12. Chapter 12: Breaking into the Sec-ML Industry 13. Index 14. Other Books You May Enjoy

An introduction to graphs

First, let us understand what graphs are and the key terms related to graphs.

What is a graph?

A graph is a data structure that is represented as a set of nodes connected by a set of edges. Mathematically, we specify a graph G as (V, E), where V represents the nodes or vertices and E represents the edges between them, as shown in Figure 8.1:

Figure 8.1 – A simple graph

Figure 8.1 – A simple graph

In the previous graph, we have the following:

V = {1, 2, 3, 4, 5, 6}

E = {(1,3), (2,3), (2,5), (3,6), (4,6), (5.6)}

Note that the order in which the nodes and edges are mentioned does not matter. The graph shown in Figure 8.1 is an undirected graph, which means that the direction of the edges does not matter. There can also be directed graphs in which the definition of the edge has some meaning, which gives importance to the direction of the edge. For example, a graph depicting the water flow of from various cities would have directed edges...

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