Cybersecurity and applied mathematics /

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Metcalf, Leigh (Autor), Casey, William (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, MA : Syngress is an imprint of Elsevier, [2016]
Temas:
Computer security > Mathematics.
Sécurité informatique > Mathématiques.
MATHEMATICS > Discrete Mathematics.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Machine generated contents note: 2.1. Introduction to Set Theory
  • 2.2. Operations on Sets
  • 2.2.1.Complement
  • 2.2.2. Intersection
  • 2.2.3. Union
  • 2.2.4. Difference
  • 2.2.5. Symmetric Difference
  • 2.2.6. Cross Product
  • 2.3. Set Theory Laws
  • 2.4. Functions
  • 2.5. Metrics
  • 2.6. Distance Variations
  • 2.6.1. Pseudometric
  • 2.6.2. Quasimetric
  • 2.6.3. Semimetric
  • 2.7. Similarities
  • 2.8. Metrics and Similarities of Numbers
  • 2.8.1. Lp Metrics
  • 2.8.2. Gaussian Kernel
  • 2.9. Metrics and Similarities of Strings
  • 2.9.1. Levenshtein Distance
  • 2.9.2. Hamming Distance
  • 2.10. Metrics and Similarities of Sets of Sets
  • 2.10.1. Jaccard Index
  • 2.10.2. Tanimoto Distance
  • 2.10.3. Overlap Coefficient
  • 2.10.4. Hausdorff Metric
  • 2.10.5. Kendall's Tau
  • 2.11. Mahalanobis Distance
  • 2.12. Internet Metrics
  • 2.12.1. Great Circle Distance
  • 2.12.2. Hop Distance
  • 2.12.3. Keyword Distance
  • 3.1. Basic Probability Review
  • 3.1.1. Language and Axioms of Probability
  • Note continued: 3.1.2.Combinatorics Aka Parlor Tricks
  • 3.1.3. Joint and Conditional Probability
  • 3.1.4. Independence and Bayes Rule
  • 3.2. From Parlor Tricks to Random Variables
  • 3.2.1. Types of Random Variables
  • 3.2.2. Properties of Random Variables
  • 3.3. The Random Variable as a Model
  • 3.3.1. Bernoulli and Geometric Distributions
  • 3.3.2. Binomial Distribution
  • 3.3.3. Poisson Distribution
  • 3.3.4. Normal Distribution
  • 3.3.5. Pareto Distributions
  • 3.3.6. Uniform Distribution
  • 3.4. Multiple Random Variables
  • 3.5. Using Probability and Random Distributions
  • 3.6. Conclusion
  • 4.1. The Language of Data Analysis
  • 4.1.1. Producing Data
  • 4.1.2. Exploratory Data Analysis
  • 4.1.3. Inference
  • 4.2. Units, Variables, and Repeated Measures
  • 4.2.1. Measurement Error and Random Variation
  • 4.3. Distributions of Data
  • 4.4. Visualizing Distributions
  • 4.4.1. Bar Plot
  • 4.4.2. Histogram
  • 4.4.3. Box Plots
  • 4.4.4. Density Plot
  • 4.5. Data Outliers
  • Note continued: 4.6. Log Transformation
  • 4.7. Parametric Families
  • 4.8. Bivariate Analysis
  • 4.8.1. Visualizing Bipartite Variables
  • 4.8.2. Correlation
  • 4.9. Time Series
  • 4.10. Classification
  • 4.11. Generating Hypotheses
  • 4.12. Conclusion
  • 5.1. An Introduction to Graph Theory
  • 5.2. Varieties of Graphs
  • 5.2.1. Undirected Graph
  • 5.2.2. Directed Graph
  • 5.2.3. Multigraph
  • 5.2.4. Bipartite Graph
  • 5.2.5. Subgraph
  • 5.2.6. Graph Complement
  • 5.3. Properties of Graphs
  • 5.3.1. Graph Sizes
  • 5.3.2. Vertices and Their Edges
  • 5.3.3. Degree
  • 5.3.4. Directed Graphs and Degrees
  • 5.3.5. Scale Free Graphs
  • 5.4. Paths, Cycles and Trees
  • 5.4.1. Paths and Cycles
  • 5.4.2. Shortest Paths
  • 5.4.3. Connected and Disconnected Graphs
  • 5.4.4. Trees
  • 5.4.5. Cycles and Their Properties
  • 5.4.6. Spanning Trees
  • 5.5. Varieties of Graphs Revisited
  • 5.5.1. Graph Density, Sparse and Dense Graphs
  • 5.5.2.Complete and Regular Graphs
  • 5.5.3. Weighted Graph
  • Note continued: 5.5.4. And Yet More Graphs!
  • 5.6. Representing Graphs
  • 5.6.1. Adjacency Matrix
  • 5.6.2. Incidence Matrix
  • 5.7. Triangles, the Smallest Cycle
  • 5.7.1. Introduction and Counting
  • 5.7.2. Triangle Free Graphs
  • 5.7.3. The Local Clustering Coefficient
  • 5.8. Distances on Graphs
  • 5.8.1. Eccentricity
  • 5.8.2. Cycle Length Properties
  • 5.9. More Properties of Graphs
  • 5.9.1. Cut
  • 5.9.2. Bridge
  • 5.9.3. Partitions
  • 5.9.4. Vertex Separators
  • 5.9.5. Cliques
  • 5.10. Centrality
  • 5.10.1. Betweenness
  • 5.10.2. Degree Centrality
  • 5.10.3. Closeness and Farness
  • 5.10.4. Cross-Clique Centrality
  • 5.11. Covering
  • 5.11.1. Vertex Covering
  • 5.11.2. Edge Cover
  • 5.12. Creating New Graphs from Old
  • 5.12.1. Union Graphs
  • 5.12.2. Intersection Graphs
  • 5.12.3. Uniting Graphs
  • 5.12.4. The Intersection Graph
  • 5.12.5. Modifying Existing Graphs
  • 5.13. Conclusion
  • 6.1. The Prisoner's Dilemma
  • 6.2. The Mathematical Definition of a Game
  • Note continued: 6.2.1. Strategies, Payoffs and Normal Form
  • 6.2.2. Normal Form
  • 6.2.3. Extensive Form
  • 6.3. Snowdrift Game
  • 6.4. Stag Hunt Game
  • 6.5. Iterative Prisoner's Dilemma
  • 6.6. Game Solutions
  • 6.6.1. Cooperative and Non-Cooperative Games
  • 6.6.2. Zero Sum Game
  • 6.6.3. Dominant Strategy
  • 6.6.4. Nash Equilibrium
  • 6.6.5. Mixed Strategy Nash Equilibrium
  • 6.7. Partially Informed Games
  • 6.8. Leader-Follower Game
  • 6.8.1. Stackelberg Game
  • 6.8.2. Colonel Blotto
  • 6.9. Signaling Games
  • 7.1. Why Visualize?
  • 7.2. What We Visualize
  • 7.2.1. Considering the Efficacy of a Visualization
  • 7.2.2. Data Collection and Visualization
  • 7.2.3. Visualizing Malware Features
  • 7.2.4. Existence Plots
  • 7.2.5.Combining Plots
  • 7.3. Visualizing IP Addresses
  • 7.3.1. Hilbert Curve
  • 7.3.2. Heat Map
  • 7.4. Plotting Higher Dimensional Data
  • 7.4.1. Principal Component Analysis
  • 7.4.2. Sammon Mapping
  • 7.5. Graph Plotting
  • 7.6. Visualizing Malware

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