Cybersecurity and Applied Mathematics - 1st Edition - ISBN: 9780128044520, 9780128044995

Cybersecurity and Applied Mathematics

1st Edition

Authors: Leigh Metcalf William Casey
eBook ISBN: 9780128044995
Paperback ISBN: 9780128044520
Imprint: Syngress
Published Date: 7th June 2016
Page Count: 240
Tax/VAT will be calculated at check-out Price includes VAT (GST)
30% off
30% off
30% off
30% off
30% off
20% off
20% off
30% off
30% off
30% off
30% off
30% off
20% off
20% off
30% off
30% off
30% off
30% off
30% off
20% off
20% off
30% off
30% off
30% off
30% off
30% off
20% off
20% off
86.32
60.42
60.42
60.42
60.42
60.42
69.06
69.06
69.95
48.97
48.97
48.97
48.97
48.97
55.96
55.96
42.99
30.09
30.09
30.09
30.09
30.09
34.39
34.39
50.95
35.66
35.66
35.66
35.66
35.66
40.76
40.76
Unavailable
Price includes VAT (GST)
× DRM-Free

Easy - Download and start reading immediately. There’s no activation process to access eBooks; all eBooks are fully searchable, and enabled for copying, pasting, and printing.

Flexible - Read on multiple operating systems and devices. Easily read eBooks on smart phones, computers, or any eBook readers, including Kindle.

Open - Buy once, receive and download all available eBook formats, including PDF, EPUB, and Mobi (for Kindle).

Institutional Access

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.

Description

Cybersecurity and Applied Mathematics explores the mathematical concepts necessary for effective cybersecurity research and practice, taking an applied approach for practitioners and students entering the field. This book covers methods of statistical exploratory data analysis and visualization as a type of model for driving decisions, also discussing key topics, such as graph theory, topological complexes, and persistent homology.

Defending the Internet is a complex effort, but applying the right techniques from mathematics can make this task more manageable. This book is essential reading for creating useful and replicable methods for analyzing data.

Key Features

  • Describes mathematical tools for solving cybersecurity problems, enabling analysts to pick the most optimal tool for the task at hand
  • Contains numerous cybersecurity examples and exercises using real world data
  • Written by mathematicians and statisticians with hands-on practitioner experience

Readership

Computer Security Architects, Engineers, Analysts, Software Developers, Incident Responders, System and Network Engineers, Penetration Testers, and Vulnerability Assessors. Advanced Undergraduate and Graduate students in Cybersecurity, Network Security, Information Technology, Computer Science, and Applied Mathematics

Table of Contents

  • Biography
  • Chapter 1: Introduction
    • Abstract
  • Chapter 2: Metrics, similarity, and sets
    • Abstract
    • 2.1 Introduction to Set Theory
    • 2.2 Operations on Sets
    • 2.3 Set Theory Laws
    • 2.4 Functions
    • 2.5 Metrics
    • 2.6 Distance Variations
    • 2.7 Similarities
    • 2.8 Metrics and Similarities of Numbers
    • 2.9 Metrics and Similarities of Strings
    • 2.10 Metrics and Similarities of Sets of Sets
    • 2.11 Mahalanobis Distance
    • 2.12 Internet Metrics
  • Chapter 3: Probability models
    • Abstract
    • 3.1 Basic Probability Review
    • 3.2 From Parlor Tricks to Random Variables
    • 3.3 The Random Variable as a Model
    • 3.4 Multiple Random Variables
    • 3.5 Using Probability and Random Distributions
    • 3.6 Conclusion
  • Chapter 4: Introduction to data analysis
    • Abstract
    • 4.1 The Language of Data Analysis
    • 4.2 Units, Variables, and Repeated Measures
    • 4.3 Distributions of Data
    • 4.4 Visualizing Distributions
    • 4.5 Data Outliers
    • 4.6 Log Transformation
    • 4.7 Parametric Families
    • 4.8 Bivariate Analysis
    • 4.9 Time Series
    • 4.10 Classification
    • 4.11 Generating Hypotheses
    • 4.12 Conclusion
  • Chapter 5: Graph theory
    • Abstract
    • 5.1 An Introduction to Graph Theory
    • 5.2 Varieties of Graphs
    • 5.3 Properties of Graphs
    • 5.4 Paths, Cycles and Trees
    • 5.5 Varieties of Graphs Revisited
    • 5.6 Representing Graphs
    • 5.7 Triangles, the Smallest Cycle
    • 5.8 Distances on Graphs
    • 5.9 More properties of graphs
    • 5.10 Centrality
    • 5.11 Covering
    • 5.12 Creating New Graphs from Old
    • 5.13 Conclusion
  • Chapter 6: Game theory
    • Abstract
    • 6.1 The Prisoner’s Dilemma
    • 6.2 The Mathematical Definition of a Game
    • 6.3 Snowdrift Game
    • 6.4 Stag Hunt Game
    • 6.5 Iterative Prisoner’s Dilemma
    • 6.6 Game Solutions
    • 6.7 Partially Informed Games
    • 6.8 Leader-Follower Game
    • 6.9 Signaling Games
  • Chapter 7: Visualizing cybersecurity data
    • Abstract
    • 7.1 Why visualize?
    • 7.2 What we visualize
    • 7.3 Visualizing IP addresses
    • 7.4 Plotting higher dimensional data
    • 7.5 Graph plotting
    • 7.6 Visualizing malware
    • 7.7 Visualizing strings
    • 7.8 Visualization with a purpose
  • Chapter 8: String analysis for cyber strings
    • Abstract
    • 8.1 String Analysis and Cyber Data
    • 8.2 Discrete String Matching
    • 8.3 Affine alignment string similarity
    • 8.4 Summary
  • Chapter 9: Persistent homology
    • Abstract
    • 9.1 Triangulations
    • 9.2 α Shapes
    • 9.3 Holes
    • 9.4 Homology
    • 9.5 Persistent homology
    • 9.6 Visualizing Persistent Homology
    • 9.7 Conclusions
  • Appendix: Introduction to linear algebra
    • A.1 Vector Algebra
    • A.2 Eigenvalues
    • A.3 Additional Matrix Operations
  • Bibliography
  • Index

Details

No. of pages:
240
Language:
English
Copyright:
© Syngress 2016
Published:
Imprint:
Syngress
eBook ISBN:
9780128044995
Paperback ISBN:
9780128044520

About the Author

Leigh Metcalf

Leigh Metcalf research’s network security, game theory, formal languages, and dynamical systems. She is Editor in Chief of the Journal on Digital Threats and has a PhD in Mathematics.

Affiliations and Expertise

PhD (Mathematics), Co-Editor in Chief, ACM Digital Threats

William Casey

Will Casey works in threat analysis, code analysis, natural language processing, genomics, bioinformatics, and applied mathematics. He has a MS and MA in Mathematics and a PhD in Applied Mathematics.

Affiliations and Expertise

PhD (Applied Mathematics)