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Intentional Risk Management through Complex Networks Analysis 1st ed. 2015 [Minkštas viršelis]

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  • Formatas: Paperback / softback, 126 pages, aukštis x plotis: 235x155 mm, weight: 2292 g, 34 Illustrations, color; XV, 126 p. 34 illus. in color., 1 Paperback / softback
  • Serija: SpringerBriefs in Optimization
  • Išleidimo metai: 21-Dec-2015
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319264214
  • ISBN-13: 9783319264219
Kitos knygos pagal šią temą:
Intentional Risk Management through Complex Networks Analysis 1st ed. 2015Didesnis paveikslėlis
  • Formatas: Paperback / softback, 126 pages, aukštis x plotis: 235x155 mm, weight: 2292 g, 34 Illustrations, color; XV, 126 p. 34 illus. in color., 1 Paperback / softback
  • Serija: SpringerBriefs in Optimization
  • Išleidimo metai: 21-Dec-2015
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319264214
  • ISBN-13: 9783319264219
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783319264219.html
Raktažodžiai:
??This book combines game theory and complex networks to examine intentional technological risk through modeling. As information security risks are in constant evolution,  the methodologies and tools to manage them must evolve to an ever-changing environment. A formal global methodology is explained  in this book, which is able to analyze risks in cyber security based on complex network models and ideas extracted from the Nash equilibrium. A risk management methodology for IT critical infrastructures is introduced which provides guidance and analysis on decision making models and real situations. This model manages the risk of succumbing to a digital attack and assesses an attack from the following three variables: income obtained, expense needed to carry out an attack, and the potential consequences for an attack. Graduate students and researchers interested in cyber security, complex network applications and intentional risk will find this book useful as it is filled with a number of models, methodologies and innovative examples.  ?
1 Intentional Risk and Cyber-Security: A Motivating Introduction
1(8)
1.1 Cyber-Attacks and Cyber-Security
1(3)
1.2 A Mathematical Model for Managing Intentional Cyber-Risk
4(2)
1.3 Incorporating Game Theory to Complex Networks
6(1)
1.4 Static Risk, Dynamic Risk and New Algorithm Optimizations
7(2)
2 Mathematical Foundations: Complex Networks and Graphs (A Review)
9(28)
2.1 Introduction
9(3)
2.1.1 Complex Systems and Complex Networks
9(1)
2.1.2 Holism vs Reductionism
10(1)
2.1.3 Complex Networks and Intentional Risk
11(1)
2.2 Basic Concepts on Graphs and Networks
12(15)
2.2.1 The Origins
12(2)
2.2.2 Graphs vs Networks
14(1)
2.2.3 Matrices, Degrees, Link Density and Some Interesting Graph Families
14(2)
2.2.4 Directed and Weighted Networks
16(2)
2.2.5 Metric Structure, Connectedness, Geodesics and Some Other Concepts
18(1)
2.2.6 Characteristic Path Length, Efficiency and Vulnerability of a Network
19(1)
2.2.7 Clustering Coefficient
20(1)
2.2.8 Finding Out the Critical and the Most Influential Nodes: Eigenvector Centrality
21(3)
2.2.9 Information Flow Management: Betweenness Centrality
24(2)
2.2.10 Degree Distributions
26(1)
2.3 Some Interesting Complex Networks Models
27(4)
2.3.1 Random Networks
27(1)
2.3.2 Small-World Model
28(1)
2.3.3 Scale-Free Networks
29(2)
2.4 New Approaches and Developments of Interest for Our Model
31(6)
2.4.1 When Edges are More Important than Nodes: Line Graph and Related Concepts
31(4)
2.4.2 Multilayer Networks
35(2)
3 Random Walkers
37(16)
3.1 An Introduction to Random Walkers
38(5)
3.2 Different Mathematical Models of Random Walkers
43(5)
3.3 Applications to Intentional Risk Analysis
48(5)
3.3.1 Accessibility and PageRank
48(1)
3.3.2 Dynamic Risk, Random Walkers and Multiplex Networks
49(4)
4 The Role of Accessibility in the Static and Dynamic Risk Computation
53(12)
4.1 Introduction: Edge's Accessibility and PageRank
53(2)
4.2 Mathematical Formulation and Notation
55(3)
4.3 Edge's PageRank via Classic PageRank
58(3)
4.4 Edge's PageRank Through Line Graph
61(1)
4.5 Classic PageRank vs Line-Graph's PageRank
62(3)
5 Mathematical Model I: Static Intentional Risk
65(34)
5.1 Intentionality Complex Network for Static Risk
65(3)
5.2 Collapsed and Nodes and Edges Assignation Algorithms
68(7)
5.2.1 0-Collapsed Algorithm and Anonymity Assignation
69(3)
5.2.2 "Max-Path" Algorithm
72(1)
5.2.3 Value Assignment Algorithm
73(1)
5.2.4 Accessibility Assignment Algorithm
74(1)
5.3 Static Risk Networks Construction from the Data
75(3)
5.3.1 Intentionality Network of Users
76(2)
5.3.2 Intentionality Network of Administrators
78(1)
5.4 Static Risk Intentionality Network Construction Method: An Example
78(18)
5.4.1 Construction Scheme
79(1)
5.4.2 Description of the Method and an Example
80(15)
5.4.3 Assignment of Attributes in the Original Network
95(1)
5.5 Final Formula and Summary of Static Risk Model
96(3)
6 Mathematical Model II: Dynamic Intentional Risk
99(4)
6.1 Comparative Analysis: Static Risk vs Dynamic Risk
99(2)
6.1.1 Accessibility in the Context of Dynamic Risk
100(1)
6.2 Dynamic Risk Model
101(2)
7 Towards the Implementation of the Model
103(18)
7.1 Modeling Access
103(5)
7.1.1 IP Source and Destination Port
103(1)
7.1.2 IP Source (e.g. 192.168.1.105) → Destination Port (e.g. 192.168.1.250:23)
103(1)
7.1.3 Restricted and Unrestricted Access Levels
104(2)
7.1.4 Static and Dynamic Risk Access Levels
106(1)
7.1.5 Static Risk with Network Protocol Analyzer Sniffers
106(1)
7.1.6 Dynamic Risk with Network Vulnerability Scanners
106(2)
7.2 Modeling Anonymity
108(1)
7.3 Determining Value
108(1)
7.4 Development of the Proof of Concept Software
108(2)
7.4.1 Software Architecture
109(1)
7.5 Proof of Concept
110(11)
7.5.1 Visualization
112(1)
7.5.2 Collapse/Expand
112(1)
7.5.3 Anonymity, Accessibility and Value
112(1)
7.5.4 Further Work for the PoC
113(8)
References 121