Math for Security
English | Tutorial | Size: 8.18 MB
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
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