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Cyber-Risk Informatics: Engineering Evaluation with Data Science [Kõva köide]

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  • Formaat: Hardback, 560 pages, kõrgus x laius x paksus: 244x163x34 mm, kaal: 898 g
  • Ilmumisaeg: 17-Jun-2016
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119087511
  • ISBN-13: 9781119087519
Teised raamatud teemal:
Cyber-Risk Informatics: Engineering Evaluation with Data ScienceSuurem pilt
  • Formaat: Hardback, 560 pages, kõrgus x laius x paksus: 244x163x34 mm, kaal: 898 g
  • Ilmumisaeg: 17-Jun-2016
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119087511
  • ISBN-13: 9781119087519
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119087519.html
Märksõnad:

This book provides a scientific modeling approach for conducting metrics-based quantitative risk assessments of cybersecurity vulnerabilities and threats.

This book provides a scientific modeling approach for conducting metrics-based quantitative risk assessments of cybersecurity threats. The author builds from a common understanding based on previous class-tested works to introduce the reader to the current and newly innovative approaches to address the maliciously-by-human-created (rather than by-chance-occurring) vulnerability and threat, and related cost-effective management to mitigate such risk. This book is purely statistical data-oriented (not deterministic) and employs computationally intensive techniques, such as Monte Carlo and Discrete Event  Simulation. The enriched JAVA ready-to-go applications and solutions to exercises provided by the author at the book’s specifically preserved website will enable readers to utilize the course related problems.

• Enables the reader to use the book's website's applications to implement and see results, and use them making ‘budgetary’ sense

• Utilizes a data analytical approach and provides clear entry points for readers of varying skill sets and backgrounds

• Developed out of necessity from real in-class experience while teaching advanced undergraduate and graduate courses by the author

Cyber-Risk Informatics is a resource for undergraduate students, graduate students, and practitioners in the field of Risk Assessment and Management regarding Security and Reliability Modeling.

Mehmet Sahinoglu, a Professor (1990) Emeritus (2000), is the founder of the Informatics Institute (2009) and its SACS-accredited (2010) and NSA-certified (2013) flagship Cybersystems and Information Security (CSIS) graduate program (the first such full degree in-class program in Southeastern USA) at AUM, Auburn University’s metropolitan campus in Montgomery, Alabama. He is a fellow member of the SDPS Society, a senior member of the IEEE, and an elected member of ISI. Sahinoglu is the recipient of Microsoft's Trustworthy Computing Curriculum (TCC) award and the author of Trustworthy Computing (Wiley, 2007).

 

Prologue xiv
Reviews xv
Preface xxi
Acknowledgments and Dedication xxix
About the Author xxxi
1 Metrics, Statistical Quality Control, and Basic Reliability in Cyber-Risk 1(60)
1.1 Deterministic and Stochastic Cyber-Risk Metrics
1(1)
1.2 Statistical Risk Analysis
2(14)
1.2.1 Introduction to Statistical Hypotheses
2(1)
1.2.2 Decision Rules
3(1)
1.2.3 One-Tailed Tests
4(1)
1.2.4 Two-Tailed Tests
4(2)
1.2.5 Decision Errors
6(1)
1.2.6 Applications to One-Tailed Tests Associated with Both Type I and Type II Errors
7(4)
1.2.7 Applications to Two-Tailed Tests (Normal Distribution Assumption)
11(5)
1.3 Acceptance Sampling in Quality Control
16(3)
1.3.1 Introduction
16(1)
1.3.2 Definition of an Acceptance Sampling Plan
16(1)
1.3.3 The OC Curve
16(3)
1.4 Poisson and Normal Approximation to Binomial in Quality Control
19(2)
1.4.1 Approximations to Binomial Distribution
19(1)
1.4.2 Approximation of Binomial to Poisson Distribution
19(1)
1.4.3 Approximation to Normal Distribution
20(1)
1.4.4 Comparisons of Normal and Poisson Approximations to the Binomial
21(1)
1.5 Basic Statistical Reliability Concepts and MC Simulators
21(31)
1.5.1 Fundamental Equations for Reliability, Hazard, and Statistical Notions
23(4)
1.5.2 Fundamentals for Reliability Block Diagramming and Redundancy
27(3)
1.5.3 Solving Basic Reliability Questions by Using Student-Friendly Pedagogical Examples
30(17)
1.5.4 MC Simulators for Commonly Used Distributions in Reliability
47(5)
1.6 Discussions and Conclusion
52(1)
1.7 Exercises
52(8)
References
60(1)
2 Complex Network Reliability Evaluation and Estimation in Cyber-Risk 61(44)
2.1 Introduction
61(1)
2.2 Overlap Technique to Calculate Complex Network Reliability
62(8)
2.2.1 Network State Enumeration and Example 1
63(1)
2.2.2 Generating Minimal Paths and Example 2
64(4)
2.2.3 Overlap Method Algorithmic Rules and Example 3
68(2)
2.3 The Overlap Method: Monte Carlo and Discrete Event Simulation
70(1)
2.4 Multistate System Reliability Evaluation
71(7)
2.4.1 Simple Series System with Single Derated States
73(1)
2.4.2 Active Parallel System
73(1)
2.4.3 Simple Series—Parallel System
74(1)
2.4.4 A Simple Series—Parallel System with Multistate Components
75(1)
2.4.5 A Combined System: Power Plant Example
76(1)
2.4.6 Large Network Examples Using Multistate Overlap Technique
77(1)
2.5 Weibull Time Distributed Reliability Evaluation
78(12)
2.5.1 Motivation behind Weibull Probability Modeling
78(1)
2.5.2 Weibull Parameter Estimation Methodology
79(1)
2.5.3 Overlap Algorithm Applied to Weibull Distributed Components
80(1)
2.5.4 Estimating Weibull Parameters
80(5)
2.5.5 Fifty-Two-Node Weibull Example for Estimating Weibull Parameters
85(5)
2.5.6 A Weibull Network Example from an Oil Rig System
90(1)
2.6 Discussions and Conclusion
90(3)
Appendix 2.A Overlap Algorithm and Example
93(8)
2.A.1 Algorithm
93(2)
2.A.2 Example
95(6)
2.7 Exercises
101(2)
References
103(2)
3 Stopping Rules for Reliability and Security Tests in Cyber-Risk 105(42)
3.1 Introduction
105(2)
3.2 Methods
107(7)
3.2.1 LGM by Verhulst
108(2)
3.2.2 Compound Poisson Model
110(4)
3.3 Examples Merging Both Stopping Rules: LGM and CPM
114(17)
3.3.1 The DR5 Data Set Example
114(4)
3.3.2 The DR4 Data Set Example
118(1)
3.3.3 The Supercomputing CLOUD Historical Failure Data—Case Study
119(2)
3.3.4 Appendix for Section 3.3
121(10)
3.4 Stopping Rule for Testing in the Time Domain
131(8)
3.4.1 Review of Compound Poisson Process and Stopping Rule
131(1)
3.4.2 Empirical Bayes Analysis for the PoissonAGeometric Stopping Rule
132(3)
3.4.3 Howden's Model for Stopping Rule
135(1)
3.4.4 Computational Example for Stopping-Rule Algorithm in Time Domain
136(3)
3.5 Discussions and Conclusion
139(4)
3.6 Exercises
143(1)
References
144(3)
4 Security Assessment and Management in Cyber-Risk 147(54)
4.1 Introduction
147(5)
4.1.1 What Other Scoring Methods Are Available?
148(4)
4.2 Security Meter (SM) Model Design
152(2)
4.3 Verification of the Probabilistic Security Meter (SM) Method by Monte Carlo Simulation and Math-Statistical Triple-Product Rule
154(16)
4.3.1 The Triple-Product Rule of Uniforms
156(2)
4.3.2 Data Analysis on the Total Residual Risk of the Security Meter Design
158(11)
4.3.3 Triple-Product Rule Discussions
169(1)
4.4 Modifying the SM Quantitative Model for Categorical, Hybrid, and Nondisjoint Data
170(8)
4.5 Maintenance Priority Determination for 3 x 3 x 2 SM
178(5)
4.6 Privacy Meter (PM): How to Quantify Privacy Breach
183(4)
4.6.1 Methodology
184(1)
4.6.2 Privacy Risk-Meter Assessment and Management Examples
185(2)
4.7 Polish Decoding (Decompression) Algorithm
187(2)
4.8 Discussions and Conclusion
189(1)
4.9 Exercises
190(9)
References
199(2)
5 Game-Theoretic Computing in Cyber-Risk 201(76)
5.1 Historical Perspective to Game Theory's Origins
201(2)
5.2 Applications of Game Theory to Cyber-Security Risk
203(1)
5.3 Intuitive Background: Concepts, Definitions, and Nomenclature
204(4)
5.3.1 A Price War Example
205(3)
5.4 Random Selection for Nash Mixed Strategy
208(5)
5.4.1 Random Probabilistic Selection
208(1)
5.4.2 Does Nash Equilibrium (NE) Exist for the Company A/B Problem in Table 5.1?
209(1)
5.4.3 An Example: Matching Pennies
210(1)
5.4.4 Another Game: The Prisoner's Dilemma
210(1)
5.4.5 Games with Multiple NE (Terrorist Game: Bold Strategy Result in Domination)
211(2)
5.5 Adversarial Risk Analysis Models by Banks, Rios, and Rios
213(2)
5.6 An Alternative Model: Sahinoglu's Security Meter for Neumann and Nash Mixed Strategy
215(5)
5.7 Other Interdisciplinary Applications of Risk Meters
220(1)
5.8 Mixed Strategy for Risk Assessment and Management-University Server and Social Network Examples
221(5)
5.8.1 University Server's Security Risk-Meter Example
221(1)
5.8.2 Social Networks' Privacy and Security Risk-Meter (RM) Example
222(2)
5.8.3 Clarification of Risk Assessment and Management Algorithm for Social Networks
224(2)
5.9 Application to Hospital Healthcare Service Risk
226(3)
5.10 Application to Environmetrics and Ecology Risk
229(5)
5.11 Application to Digital Forensics Security Risk
234(5)
5.12 Application to Business Contracting Risk
239(6)
5.13 Application to National Cybersecurity Risk
245(8)
5.14 Application to Airport Service Quality Risk
253(4)
5.15 Application to Offshore Oil-Drilling Spill and Security Risk
257(7)
5.16 Discussions and Conclusion
264(2)
5.17 Exercises
266(5)
References
271(6)
6 Modeling and Simulation in Cyber-Risk 277(62)
6.1 Introduction and a Brief History to Simulation
277(1)
6.2 Generic Theory: Case Studies on Goodness of Fit for Uniform Numbers
278(1)
6.3 Why Crucial to Manufacturing and Cyber Defense
279(1)
6.4 A Cross Section of Modeling and Simulation in Manufacturing Industry
280(21)
6.4.1 Modeling and Simulation of Multistate Production Units and Systems in Manufacturing
281(2)
6.4.2 Two-State SL Probability Model of Units with Closed-Form Solution
283(1)
6.4.3 Extended Three-State SL Probability Model of UP—DOWN—DERATED Units with MC Simulation
284(5)
6.4.4 Statistical Simulation of Three-State Units to Estimate the Density of UP—DOWN—DER
289(7)
6.4.5 How to Generate Random Numbers from SL pdf to Simulate Component and System Behavior
296(1)
6.4.6 Example of SL Simulation for Modeling Network of 2-in-Simple-Series Two-State (UP—DN) Units
297(3)
6.4.7 Example of SL Simulation for Modeling a Network of 7-in-Complex-Topology Two-State (UP—DN) Units
300(1)
6.5 A Review of Modeling and Simulation in Cyber-Security
301(5)
6.5.1 MC Value-at-Risk Approach by Kim et al. in CLOUD Computing
301(1)
6.5.2 MC and DES in Security Meter (SM) Risk Model
302(4)
6.6 Application of Queuing Theory and Multichannel Simulation to Cyber-Security
306(2)
6.6.1 Example 1: One Recovery-Crew Case for Cyber-Security Queuing Simulation
306(2)
6.6.2 Example 2: Two Recovery-Crew Case for Cyber-Security Queuing Simulation
308(1)
6.7 Discussions and Conclusion
308(3)
Appendix 6.A
311(4)
6.8 Exercises
315(20)
References
335(4)
7 CLOUD Computing in Cyber-Risk 339(82)
7.1 Introduction and Motivation
339(3)
7.2 CLOUD Computing Risk Assessment
342(1)
7.3 Motivation and Methodology
343(6)
7.3.1 History of Theoretical Developments on CLOUD Modeling
343(1)
7.3.2 Notation
344(1)
7.3.3 Objectives
344(1)
7.3.4 Frequency and Duration Method for the Loss of Load or Service
345(1)
7.3.5 NBD as a Compound Poisson Model
346(2)
7.3.6 NBD for the Loss of Load or Loss of CLOUD Service Expected
348(1)
7.4 Various Applications to Cyber Systems
349(8)
7.4.1 Small Sample Experimental Systems
349(4)
7.4.2 Large Cyber Systems
353(4)
7.5 Large Cyber Systems Using Statistical Methods
357(2)
7.6 Repair Crew and Product Reserve Planning to Manage Risk Cost Effectively Using Cyberrisksolver CLOUD Management Java Tool
359(9)
7.6.1 CLOUD Resource Management Planning for Employment of Repair Crews
360(5)
7.6.2 CLOUD Resource Management Planning by Production Deployment
365(3)
7.7 Remarks for "Physical CLOUD" Employing Physical Products Servers, Generators, Communication Towers, Etc.)
368(4)
7.8 Applications to "Social (Human Resources) CLOUD"
372(7)
7.8.1 Numerical Example for Social CLOUD (200 Employees Performing)
376(3)
7.8.2 Input Wizard Example for Social CLOUD (200 Employees Performing)
379(1)
7.9 Stochastic CLOUD System Simulation
379(18)
7.9.1 Introduction and Methodology
381(4)
7.9.2 Numerical Applications for SS to Verify Non-SS
385(2)
7.9.3 Details of Probability Distributions Used in Stochastic Simulation
387(6)
7.9.4 Varying Product Repair and Failure Date with Empirical Bayesian Posterior Gamma Approach
393(1)
7.9.5 Varying Link Repair and Failure Using Gamma Distribution
393(1)
7.9.6 SS Applied to a Power or Cyber Grid
394(2)
7.9.7 Error Checking or Flagging
396(1)
7.10 CLOUD Risk Meter Analysis
397(8)
7.10.1 Risk Assessment and Management Clarifications for Figures 7.72 and 7.73
402(3)
7.11 Discussions and Conclusion
405(2)
7.12 Exercises
407(9)
References
416(5)
8 Software Reliability Modeling and Metrics in Cyber-Risk 421(30)
8.1 Introduction, Motivation, and Methodology
421(1)
8.2 History and Classification of Software Reliability Models
422(2)
8.2.1 Time-between-Failures Models
422(1)
8.2.2 Failure-Counting Models
422(1)
8.2.3 Bayesian Model
423(1)
8.2.4 Static (Nondynamic) Models
423(1)
8.2.5 Others
424(1)
8.3 Software Reliability Models in Time Domain
424(1)
8.4 Software Reliability Growth Models
425(15)
8.4.1 Negative Exponential Class of Failure Times
425(1)
8.4.2 J—M De-eutrophication Model (Binomial Type)
425(1)
8.4.3 Moranda's Geometric Model (Poisson Type)
426(1)
8.4.4 Goel—Okumoto Nonhomogeneous Poisson Process (Poisson Type)
427(1)
8.4.5 Musa's Basic Execution Time Model (Poisson Type)
428(1)
8.4.6 Musa—Okumoto Logarithmic Poisson Execution Time Model (Poisson Type)
429(2)
8.4.7 L—V Bayesian Model
431(2)
8.4.8 Sahinoglu's Compound PoissonAGeometric and PoissonALogarithmic Series Models
433(2)
8.4.9 Gamma, Weibull, and Other Classes of Failure Times
435(4)
8.4.10 Duane Model (Poisson Type)
439(1)
8.5 Numerical Examples Using Pedagogues
440(1)
8.5.1 Example 1
440(1)
8.5.2 Example 2
441(1)
8.6 Recent Trends in Software Reliability
441(1)
8.7 Discussions and Conclusion
442(3)
8.8 Exercises
References
445(6)
9 Metrics for Software Reliability Failure-Count Models in Cyber-Risk 451(32)
9.1 Introduction and Methodology on Failure-Count Estimation in Software Reliability
451(15)
9.1.1 Statistical Estimation Models, Computational Formulas, and Examples
452(12)
9.1.2 Interpretations of Numerical Examples and Discussions
464(2)
9.2 Predictive Accuracy to Compare Failure-Count Models
466(7)
9.2.1 Classical Distribution Approach
468(1)
9.2.2 Prior Distribution Approach
469(3)
9.2.3 Applications to Data Sets and Comparisons
472(1)
9.3 Discussions and Conclusion
473(4)
Appendix 9.A
477(1)
9.4 Exercises
478(4)
References
482(1)
10 Practical Hands-On Lab Topics in Cyber-Risk 483(28)
10.1 System Hardening
483(3)
10.1.1 General
483(1)
10.1.2 Windows Servers
484(1)
10.1.3 Wireless
484(1)
10.1.4 Firewalls, Routers, and Switches
485(1)
10.2 Email Security
486(1)
10.2.1 Identifying Fake Emails
486(1)
10.2.2 Emotion Responses
486(1)
10.3 MS-DOS Commands
487(5)
10.3.1 Mapping Intel
488(4)
10.4 Logging
492(3)
10.4.1 Policy
493(1)
10.4.2 Understanding Logs
494(1)
10.5 Firewall
495(1)
10.5.1 Traditional Firewalls
495(1)
10.5.2 NGFs
496(1)
10.5.3 Host-Based Firewalls
496(1)
10.6 Wireless Networks
496(3)
10.7 Discussions and Conclusion
499(1)
Appendix 10.A
500(1)
10.8 Exercises
501(8)
10.8.1 System Hardening
501(1)
10.8.2 Email
501(1)
10.8.3 MS-DOS
502(1)
10.8.4 Logging
503(1)
10.8.5 Firewall
503(2)
10.8.6 Wireless
505(1)
10.8.7 Comprehensive Exercises
505(2)
10.8.8 Cryptology Projects
507(2)
References
509(2)
What the Cyber-Risk Informatics Textbook and the Author are About? 511(2)
Index 513
Mehmet Sahinoglu, a Professor (1990) Emeritus (2000), is the founder of the Informatics Institute (2009) and its SACS-accredited (2010) and NSA-certified (2013) flagship Cybersystems and Information Security (CSIS) graduate program (the first such full degree in-class program in Southeastern USA) at AUM, Auburn Universitys metropolitan campus in Montgomery, Alabama. He is a fellow member of the SDPS Society, a senior member of the IEEE, and an elected member of ISI. Sahinoglu is the recipient of Microsoft's Trustworthy Computing Curriculum (TCC) award and the author of Trustworthy Computing (Wiley, 2007).