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Math for Security: From Graphs and Geometry to Spatial Analysis, Daniel Reilly
Math for Security: From Graphs and Geometry to Spatial Analysis, Daniel Reilly
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МоваEnglish
ОбкладинкаМ'яка
Папірбіла, офсет
Рік2023
Станнова книга

Use applied math to map fire stations, develop facial recognition software, solve the art gallery problem and more in this hands-on, real-world infosec book.

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

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Math for Security: From Graphs and Geometry to Spatial Analysis, Daniel Reilly

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