Math for Security Cover

Math for Security

From Graphs and Geometry to Spatial Analysis
by Daniel Reilly
September 2023, 312 pp
ISBN-13: 
9781718502567
Lay-flat binding

Download Chapter 3: Securing Networks With Graph Theory

Look Inside!

Math for Security pages 42-43Math for Security pages 74-75Math for Security pages 86-87Math for Security pages 100-101Math for Security pages 158-159

Explore the intersection of mathematics and computer security with this engaging and accessible guide.

Math for Security will equip you with essential tools to tackle complex security problems head on. All you need are some basic programming skills. Once you’ve set up your development environment and reviewed the necessary Python syntax and math notation in the early chapters, you’ll dive deep into practical applications, leveraging the power of math to analyze networks, optimize resource distribution, and much more. In the book’s final chapters, you’ll take your projects from proof of concepts to viable applications and explore options for delivering them to end users.

As you work through various security scenarios, you’ll:

  • Employ packet analysis and graph theory to detect data exfiltration attempts in a network
  • Predict potential targets and find weaknesses in social networks with Monte Carlo simulations
  • Use basic geometry and OpenCell data to triangulate a phone’s location without GPS
  • Apply computational geometry to Voronoi diagrams for use in emergency service planning
  • Train a facial recognition system with machine learning for real-time identity verification
  • Use spatial analysis to distribute physical security features effectively in an art gallery

Whether you’re an aspiring security professional, a social network analyst, or an innovator seeking to create cutting-edge security solutions, this book will empower you to solve complex problems with precision and confidence. Embrace the intricate world of math as your secret weapon in computer security!

Covers Python 3.x

Author Bio 

Daniel Reilly is a security researcher, analyst, and consultant based out of Seattle, WA. He has worked in the security field for 20 years, more than half of which has been spent developing and managing operational security for small businesses.

Table of contents 

Introduction
PART I: ENVIRONMENT and CONVENTIONS
Chapter 1: Setting up the Environment
Chapter 2: Programming and Math Conventions
Part II: GRAPH THEORY AND COMPUTATIONAL GEOMETRY
Chapter 3: Network and Graph Theory
Chapter 4: Building a Network Analysis Graph 
Chapter 5: Analyzing Social Networks Derived from Mastodone Posts
Chapter 6: Analyzing Social Network Evolution with Monte Carlo Simulations
Chapter 7: Computational Geometry Theory
Chapter 8: Triangulating Locations from OpenCell Data
Chapter 9: Emergency Service Planning with Voronoi Diagrams
Chapter 10: Computational Geometry for Facial Recognition
PART III: THE ART GALLERY PROBLEM
Chapter 11: Understanding the Art Gallery Problem
Chapter 12: Going Beyond the Proof of Concept
Chapter 13: Delivering Python Applications
Endnotes
Index

View the detailed Table of Contents
View the Index

 

Reviews 

"A very practical book for security. . . . a real eye-opener."
—William Gasarch, Professor, University of Maryland-Dept of Computer Science

"A really nice introduction to graph theory and computational geometry for people who know a bit of Python and without a mathematical background."
—Julien Voisin, Artificial Truth

"The book was very easy to follow, I'd expect anyone with a technical or stats background to be able to dive right in given the step-by-step instructions and explanations provided by Daniel."
—@WithSandra, tech YouTuber and security analyst

"Whether you're an aspiring security professional, a social network analyst, or an innovator seeking to create cutting-edge security solutions, Math for Security will empower you to solve complex problems with precision and confidence. "
—Midwest Book Review

Extra Stuff 

Click here to access the book's online resources. 

Read an interview with the author on the Computational Complexity blog.