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 xivReviews xvPreface xxiAcknowledgments and Dedication xxixAbout the Author xxxi1 Metrics, Statistical Quality Control, and Basic Reliability in Cyber-Risk 11.1 Deterministic and Stochastic Cyber-Risk Metrics 11.2 Statistical Risk Analysis 21.2.1 Introduction to Statistical Hypotheses 21.2.2 Decision Rules 31.2.3 One-Tailed Tests 41.2.4 Two-Tailed Tests 41.2.5 Decision Errors 61.2.6 Applications to One-Tailed Tests Associated with Both Type I and Type II Errors 71.2.7 Applications to Two-Tailed Tests (Normal Distribution Assumption) 111.3 Acceptance Sampling in Quality Control 161.3.1 Introduction 161.3.2 Definition of an Acceptance Sampling Plan 161.3.3 The OC Curve 161.4 Poisson and Normal Approximation to Binomial in Quality Control 191.4.1 Approximations to Binomial Distribution 191.4.2 Approximation of Binomial to Poisson Distribution 191.4.3 Approximation to Normal Distribution 201.4.4 Comparisons of Normal and Poisson Approximations to the Binomial 211.5 Basic Statistical Reliability Concepts and Mc Simulators 211.5.1 Fundamental Equations for Reliability, Hazard, and Statistical Notions 231.5.2 Fundamentals for Reliability Block Diagramming and Redundancy 271.5.3 Solving Basic Reliability Questions by Using Student-Friendly Pedagogical Examples 301.5.4 MC Simulators for Commonly Used Distributions in Reliability 471.6 Discussions and Conclusion 521.7 Exercises 52References 602 Complex Network Reliability Evaluation and Estimation in Cyber-Risk 612.1 Introduction 612.2 Overlap Technique to Calculate Complex Network Reliability 622.2.1 Network State Enumeration and Example 1 632.2.2 Generating Minimal Paths and Example 2 642.2.3 Overlap Method Algorithmic Rules and Example 3 682.3 The Overlap Method: Monte Carlo and Discrete Event Simulation 702.4 Multistate System Reliability Evaluation 712.4.1 Simple Series System with Single Derated States 732.4.2 Active Parallel System 732.4.3 Simple Series–Parallel System 742.4.4 A Simple Series–Parallel System with Multistate Components 752.4.5 A Combined System: Power Plant Example 762.4.6 Large Network Examples Using Multistate Overlap Technique 772.5 Weibull Time Distributed Reliability Evaluation 782.5.1 Motivation behind Weibull Probability Modeling 782.5.2 Weibull Parameter Estimation Methodology 792.5.3 Overlap Algorithm Applied to Weibull Distributed Components 802.5.4 Estimating Weibull Parameters 802.5.5 Fifty-Two-Node Weibull Example for Estimating Weibull Parameters 852.5.6 A Weibull Network Example from an Oil Rig System 902.6 Discussions and Conclusion 90Appendix 2.A Overlap Algorithm and Example 932.A.1 Algorithm 932.A.2 Example 952.7 Exercises 101References 1033 Stopping Rules for Reliability and Security Tests in Cyber-Risk 1053.1 Introduction 1053.2 Methods 1073.2.1 Lgm by Verhulst 1083.2.2 Compound Poisson Model 1103.3 Examples Merging Both Stopping Rules: Lgm and Cpm 1143.3.1 The DR5 Data Set Example 1143.3.2 The Dr4 Data Set Example 1183.3.3 The Supercomputing Cloud Historical Failure Data—Case Study 1193.3.4 Appendix for Section 3.3 1213.4 Stopping Rule for Testing in the Time Domain 1313.4.1 Review of Compound Poisson Process and Stopping Rule 1313.4.2 Empirical Bayes Analysis for the Poisson^Geometric Stopping Rule 1323.4.3 Howden’s Model for Stopping Rule 1353.4.4 Computational Example for Stopping-Rule Algorithm in Time Domain 1363.5 Discussions and Conclusion 1393.6 Exercises 143References 1444 Security Assessment and Management in Cyber-Risk 1474.1 Introduction 1474.1.1 What Other Scoring Methods Are Available? 1484.2 Security Meter (Sm) Model Design 1524.3 Verification of the Probabilistic Security Meter (Sm) Method by Monte Carlo Simulation and Math-Statistical Triple-Product Rule 1544.3.1 The Triple-Product Rule of Uniforms 1564.3.2 Data Analysis on the Total Residual Risk of the Security Meter Design 1584.3.3 Triple-Product Rule Discussions 1694.4 Modifying the SM Quantitative Model for Categorical, Hybrid, and Nondisjoint Data 1704.5 Maintenance Priority Determination for 3 × 3 × 2 Sm 1784.6 Privacy Meter (PM): How to Quantify Privacy Breach 1834.6.1 Methodology 1844.6.2 Privacy Risk-Meter Assessment and Management Examples 1854.7 Polish Decoding (Decompression) Algorithm 1874.8 Discussions and Conclusion 1894.9 Exercises 190References 1995 Game-Theoretic Computing in Cyber-Risk 2015.1 Historical Perspective to Game Theory’s Origins 2015.2 Applications of Game Theory to Cyber-Security Risk 2035.3 Intuitive Background: Concepts, Definitions, and Nomenclature 2045.3.1 A Price War Example 2055.4 Random Selection for Nash Mixed Strategy 2085.4.1 Random Probabilistic Selection 2085.4.2 Does Nash Equilibrium (NE) Exist for the Company A/B Problem in Table 5.1? 2095.4.3 An Example: Matching Pennies 2105.4.4 Another Game: The Prisoner’s Dilemma 2105.4.5 Games with Multiple NE (Terrorist Game: Bold Strategy Result in Domination) 2115.5 Adversarial Risk Analysis Models by Banks, Rios, and Rios 2135.6 An Alternative Model: Sahinoglu’s Security Meter for Neumann and Nash Mixed Strategy 2155.7 Other Interdisciplinary Applications of Risk Meters 2205.8 Mixed Strategy for Risk Assessment and Management-University Server and Social Network Examples 2215.8.1 University Server’s Security Risk-Meter Example 2215.8.2 Social Networks’ Privacy and Security Risk-Meter (RM) Example 2225.8.3 Clarification of Risk Assessment and Management Algorithm for Social Networks 2245.9 Application to Hospital Healthcare Service Risk 2265.10 Application to Environmetrics and Ecology Risk 2295.11 Application to Digital Forensics Security Risk 2345.12 Application to Business Contracting Risk 2395.13 Application to National Cybersecurity Risk 2455.14 Application to Airport Service Quality Risk 2535.15 Application to Offshore Oil-Drilling Spill and Security Risk 2575.16 Discussions and Conclusion 2645.17 Exercises 266References 2716 Modeling and Simulation in Cyber-Risk 2776.1 Introduction and a Brief History to Simulation 2776.2 Generic Theory: Case Studies on Goodness of Fit for Uniform Numbers 2786.3 Why Crucial to Manufacturing and Cyber Defense 2796.4 A Cross Section of Modeling and Simulation in Manufacturing Industry 2806.4.1 Modeling and Simulation of Multistate Production Units and Systems in Manufacturing 2816.4.2 Two-State SL Probability Model of Units with Closed-Form Solution 2836.4.3 Extended Three-State SL Probability Model of Up–Down –Derated Units with Mc Simulation 2846.4.4 Statistical Simulation of Three-State Units to Estimate the Density of Up–Down –Der 2896.4.5 How to Generate Random Numbers from Sl pdf to Simulate Component and System Behavior 2966.4.6 Example of Sl Simulation for Modeling Network of 2-in-Simple-Series Two-State (Up–Dn) Units 2976.4.7 Example of Sl Simulation for Modeling a Network of 7-in-Complex-Topology Two-State (Up–Dn) Units 3006.5 A Review of Modeling and Simulation in Cyber-Security 3016.5.1 MC Value-at-Risk Approach by Kim et al. in Cloud Computing 3016.5.2 MC and DES in Security Meter (Sm) Risk Model 3026.6 Application of Queuing Theory and Multichannel Simulation to Cyber-Security 3066.6.1 Example 1: One Recovery-Crew Case for Cyber-Security Queuing Simulation 3066.6.2 Example 2: Two Recovery-Crew Case for Cyber-Security Queuing Simulation 3086.7 Discussions and Conclusion 308Appendix 6.A 3116.8 Exercises 315References 3357 Cloud Computing in Cyber-Risk 3397.1 Introduction and Motivation 3397.2 Cloud Computing Risk Assessment 3427.3 Motivation and Methodology 3437.3.1 History of Theoretical Developments on CLOUD Modeling 3437.3.2 Notation 3447.3.3 Objectives 3447.3.4 Frequency and Duration Method for the Loss of Load or Service 3457.3.5 Nbd as a Compound Poisson Model 3467.3.6 Nbd for the Loss of Load or Loss of Cloud Service Expected 3487.4 Various Applications to Cyber Systems 3497.4.1 Small Sample Experimental Systems 3497.4.2 Large Cyber Systems 3537.5 Large Cyber Systems Using Statistical Methods 3577.6 Repair Crew and Product Reserve Planning to Manage Risk Cost Effectively Using Cyberrisksolver Cloud Management Java Tool 3597.6.1 Cloud Resource Management Planning for Employment of Repair Crews 3607.6.2 Cloud Resource Management Planning by Production Deployment 3657.7 Remarks for “Physical Cloud” Employing Physical Products (Servers, Generators, Communication Towers, Etc.) 3687.8 Applications to “Social (Human Resources) Cloud” 3727.8.1 Numerical Example for Social Cloud (200 Employees Performing) 3767.8.2 Input Wizard Example for Social Cloud (200 Employees Performing) 3797.9 Stochastic Cloud System Simulation 3797.9.1 Introduction and Methodology 3817.9.2 Numerical Applications for Ss to Verify Non-Ss 3857.9.3 Details of Probability Distributions Used in Stochastic Simulation 3877.9.4 Varying Product Repair and Failure Date with Empirical Bayesian Posterior Gamma Approach 3937.9.5 Varying Link Repair and Failure Using Gamma Distribution 3937.9.6 Ss Applied to a Power or Cyber Grid 3947.9.7 Error Checking or Flagging 3967.10 Cloud Risk Meter Analysis 3977.10.1 Risk Assessment and Management Clarifications for Figures 7.72 and 7.73 4027.11 Discussions and Conclusion 4057.12 Exercises 407References 4168 Software Reliability Modeling and Metrics in Cyber-Risk 4218.1 Introduction, Motivation, and Methodology 4218.2 History and Classification of Software Reliability Models 4228.2.1 Time-between-Failures Models 4228.2.2 Failure-Counting Models 4228.2.3 Bayesian Model 4238.2.4 Static (Nondynamic) Models 4238.2.5 Others 4248.3 Software Reliability Models in Time Domain 4248.4 Software Reliability Growth Models 4258.4.1 Negative Exponential Class of Failure Times 4258.4.2 J–M De-eutrophication Model (Binomial Type) 4258.4.3 Moranda’s Geometric Model (Poisson Type) 4268.4.4 Goel–Okumoto Nonhomogeneous Poisson Process (Poisson Type) 4278.4.5 Musa’s Basic Execution Time Model (Poisson Type) 4288.4.6 Musa–Okumoto Logarithmic Poisson Execution Time Model (Poisson Type) 4298.4.7 L–V Bayesian Model 4318.4.8 Sahinoglu’s Compound Poisson^Geometric and Poisson^Logarithmic Series Models 4338.4.9 Gamma, Weibull, and Other Classes of Failure Times 4358.4.10 Duane Model (Poisson Type) 4398.5 Numerical Examples Using Pedagogues 4408.5.1 Example 1 4408.5.2 Example 2 4418.6 Recent Trends in Software Reliability 4418.7 Discussions and Conclusion 4428.8 Exercises 444References 4459 Metrics for Software Reliability Failure-Count Models in Cyber-Risk 4519.1 Introduction and Methodology on Failure-Count Estimation in Software Reliability 4519.1.1 Statistical Estimation Models, Computational Formulas, and Examples 4529.1.2 Interpretations of Numerical Examples and Discussions 4649.2 Predictive Accuracy to Compare Failure-Count Models 4669.2.1 Classical Distribution Approach 4689.2.2 Prior Distribution Approach 4699.2.3 Applications to Data Sets and Comparisons 4729.3 Discussions and Conclusion 473appendix 9.A 4779.4 Exercises 478References 48210 Practical Hands-On Lab Topics in Cyber-Risk 48310.1 System Hardening 48310.1.1 General 48310.1.2 Windows Servers 48410.1.3 Wireless 48410.1.4 Firewalls, Routers, and Switches 48510.2 Email Security 48610.2.1 Identifying Fake Emails 48610.2.2 Emotion Responses 48610.3 MS-DOS Commands 48710.3.1 Mapping Intel 48810.4 Logging 49210.4.1 Policy 49310.4.2 Understanding Logs 49410.5 Firewall 49510.5.1 Traditional Firewalls 49510.5.2 Ngfs 49610.5.3 Host-Based Firewalls 49610.6 Wireless Networks 49610.7 Discussions and Conclusion 499Appendix 10.A 50010.8 Exercises 50110.8.1 System Hardening 50110.8.2 Email 50110.8.3 Ms-Dos 50210.8.4 Logging 50310.8.5 Firewall 50310.8.6 Wireless 50510.8.7 Comprehensive Exercises 50510.8.8 Cryptology Projects 507References 509What the Cyber-Risk Informatics Textbook and the Author are About? 511Index 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 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 xivReviews xvPreface xxiAcknowledgments and Dedication xxixAbout the Author xxxi1 Metrics, Statistical Quality Control, and Basic Reliability in Cyber-Risk 11.1 Deterministic and Stochastic Cyber-Risk Metrics 11.2 Statistical Risk Analysis 21.2.1 Introduction to Statistical Hypotheses 21.2.2 Decision Rules 31.2.3 One-Tailed Tests 41.2.4 Two-Tailed Tests 41.2.5 Decision Errors 61.2.6 Applications to One-Tailed Tests Associated with Both Type I and Type II Errors 71.2.7 Applications to Two-Tailed Tests (Normal Distribution Assumption) 111.3 Acceptance Sampling in Quality Control 161.3.1 Introduction 161.3.2 Definition of an Acceptance Sampling Plan 161.3.3 The OC Curve 161.4 Poisson and Normal Approximation to Binomial in Quality Control 191.4.1 Approximations to Binomial Distribution 191.4.2 Approximation of Binomial to Poisson Distribution 191.4.3 Approximation to Normal Distribution 201.4.4 Comparisons of Normal and Poisson Approximations to the Binomial 211.5 Basic Statistical Reliability Concepts and Mc Simulators 211.5.1 Fundamental Equations for Reliability, Hazard, and Statistical Notions 231.5.2 Fundamentals for Reliability Block Diagramming and Redundancy 271.5.3 Solving Basic Reliability Questions by Using Student-Friendly Pedagogical Examples 301.5.4 MC Simulators for Commonly Used Distributions in Reliability 471.6 Discussions and Conclusion 521.7 Exercises 52References 602 Complex Network Reliability Evaluation and Estimation in Cyber-Risk 612.1 Introduction 612.2 Overlap Technique to Calculate Complex Network Reliability 622.2.1 Network State Enumeration and Example 1 632.2.2 Generating Minimal Paths and Example 2 642.2.3 Overlap Method Algorithmic Rules and Example 3 682.3 The Overlap Method: Monte Carlo and Discrete Event Simulation 702.4 Multistate System Reliability Evaluation 712.4.1 Simple Series System with Single Derated States 732.4.2 Active Parallel System 732.4.3 Simple Series–Parallel System 742.4.4 A Simple Series–Parallel System with Multistate Components 752.4.5 A Combined System: Power Plant Example 762.4.6 Large Network Examples Using Multistate Overlap Technique 772.5 Weibull Time Distributed Reliability Evaluation 782.5.1 Motivation behind Weibull Probability Modeling 782.5.2 Weibull Parameter Estimation Methodology 792.5.3 Overlap Algorithm Applied to Weibull Distributed Components 802.5.4 Estimating Weibull Parameters 802.5.5 Fifty-Two-Node Weibull Example for Estimating Weibull Parameters 852.5.6 A Weibull Network Example from an Oil Rig System 902.6 Discussions and Conclusion 90Appendix 2.A Overlap Algorithm and Example 932.A.1 Algorithm 932.A.2 Example 952.7 Exercises 101References 1033 Stopping Rules for Reliability and Security Tests in Cyber-Risk 1053.1 Introduction 1053.2 Methods 1073.2.1 Lgm by Verhulst 1083.2.2 Compound Poisson Model 1103.3 Examples Merging Both Stopping Rules: Lgm and Cpm 1143.3.1 The DR5 Data Set Example 1143.3.2 The Dr4 Data Set Example 1183.3.3 The Supercomputing Cloud Historical Failure Data—Case Study 1193.3.4 Appendix for Section 3.3 1213.4 Stopping Rule for Testing in the Time Domain 1313.4.1 Review of Compound Poisson Process and Stopping Rule 1313.4.2 Empirical Bayes Analysis for the Poisson^Geometric Stopping Rule 1323.4.3 Howden’s Model for Stopping Rule 1353.4.4 Computational Example for Stopping-Rule Algorithm in Time Domain 1363.5 Discussions and Conclusion 1393.6 Exercises 143References 1444 Security Assessment and Management in Cyber-Risk 1474.1 Introduction 1474.1.1 What Other Scoring Methods Are Available? 1484.2 Security Meter (Sm) Model Design 1524.3 Verification of the Probabilistic Security Meter (Sm) Method by Monte Carlo Simulation and Math-Statistical Triple-Product Rule 1544.3.1 The Triple-Product Rule of Uniforms 1564.3.2 Data Analysis on the Total Residual Risk of the Security Meter Design 1584.3.3 Triple-Product Rule Discussions 1694.4 Modifying the SM Quantitative Model for Categorical, Hybrid, and Nondisjoint Data 1704.5 Maintenance Priority Determination for 3 × 3 × 2 Sm 1784.6 Privacy Meter (PM): How to Quantify Privacy Breach 1834.6.1 Methodology 1844.6.2 Privacy Risk-Meter Assessment and Management Examples 1854.7 Polish Decoding (Decompression) Algorithm 1874.8 Discussions and Conclusion 1894.9 Exercises 190References 1995 Game-Theoretic Computing in Cyber-Risk 2015.1 Historical Perspective to Game Theory’s Origins 2015.2 Applications of Game Theory to Cyber-Security Risk 2035.3 Intuitive Background: Concepts, Definitions, and Nomenclature 2045.3.1 A Price War Example 2055.4 Random Selection for Nash Mixed Strategy 2085.4.1 Random Probabilistic Selection 2085.4.2 Does Nash Equilibrium (NE) Exist for the Company A/B Problem in Table 5.1? 2095.4.3 An Example: Matching Pennies 2105.4.4 Another Game: The Prisoner’s Dilemma 2105.4.5 Games with Multiple NE (Terrorist Game: Bold Strategy Result in Domination) 2115.5 Adversarial Risk Analysis Models by Banks, Rios, and Rios 2135.6 An Alternative Model: Sahinoglu’s Security Meter for Neumann and Nash Mixed Strategy 2155.7 Other Interdisciplinary Applications of Risk Meters 2205.8 Mixed Strategy for Risk Assessment and Management-University Server and Social Network Examples 2215.8.1 University Server’s Security Risk-Meter Example 2215.8.2 Social Networks’ Privacy and Security Risk-Meter (RM) Example 2225.8.3 Clarification of Risk Assessment and Management Algorithm for Social Networks 2245.9 Application to Hospital Healthcare Service Risk 2265.10 Application to Environmetrics and Ecology Risk 2295.11 Application to Digital Forensics Security Risk 2345.12 Application to Business Contracting Risk 2395.13 Application to National Cybersecurity Risk 2455.14 Application to Airport Service Quality Risk 2535.15 Application to Offshore Oil-Drilling Spill and Security Risk 2575.16 Discussions and Conclusion 2645.17 Exercises 266References 2716 Modeling and Simulation in Cyber-Risk 2776.1 Introduction and a Brief History to Simulation 2776.2 Generic Theory: Case Studies on Goodness of Fit for Uniform Numbers 2786.3 Why Crucial to Manufacturing and Cyber Defense 2796.4 A Cross Section of Modeling and Simulation in Manufacturing Industry 2806.4.1 Modeling and Simulation of Multistate Production Units and Systems in Manufacturing 2816.4.2 Two-State SL Probability Model of Units with Closed-Form Solution 2836.4.3 Extended Three-State SL Probability Model of Up–Down –Derated Units with Mc Simulation 2846.4.4 Statistical Simulation of Three-State Units to Estimate the Density of Up–Down –Der 2896.4.5 How to Generate Random Numbers from Sl pdf to Simulate Component and System Behavior 2966.4.6 Example of Sl Simulation for Modeling Network of 2-in-Simple-Series Two-State (Up–Dn) Units 2976.4.7 Example of Sl Simulation for Modeling a Network of 7-in-Complex-Topology Two-State (Up–Dn) Units 3006.5 A Review of Modeling and Simulation in Cyber-Security 3016.5.1 MC Value-at-Risk Approach by Kim et al. in Cloud Computing 3016.5.2 MC and DES in Security Meter (Sm) Risk Model 3026.6 Application of Queuing Theory and Multichannel Simulation to Cyber-Security 3066.6.1 Example 1: One Recovery-Crew Case for Cyber-Security Queuing Simulation 3066.6.2 Example 2: Two Recovery-Crew Case for Cyber-Security Queuing Simulation 3086.7 Discussions and Conclusion 308Appendix 6.A 3116.8 Exercises 315References 3357 Cloud Computing in Cyber-Risk 3397.1 Introduction and Motivation 3397.2 Cloud Computing Risk Assessment 3427.3 Motivation and Methodology 3437.3.1 History of Theoretical Developments on CLOUD Modeling 3437.3.2 Notation 3447.3.3 Objectives 3447.3.4 Frequency and Duration Method for the Loss of Load or Service 3457.3.5 Nbd as a Compound Poisson Model 3467.3.6 Nbd for the Loss of Load or Loss of Cloud Service Expected 3487.4 Various Applications to Cyber Systems 3497.4.1 Small Sample Experimental Systems 3497.4.2 Large Cyber Systems 3537.5 Large Cyber Systems Using Statistical Methods 3577.6 Repair Crew and Product Reserve Planning to Manage Risk Cost Effectively Using Cyberrisksolver Cloud Management Java Tool 3597.6.1 Cloud Resource Management Planning for Employment of Repair Crews 3607.6.2 Cloud Resource Management Planning by Production Deployment 3657.7 Remarks for “Physical Cloud” Employing Physical Products (Servers, Generators, Communication Towers, Etc.) 3687.8 Applications to “Social (Human Resources) Cloud” 3727.8.1 Numerical Example for Social Cloud (200 Employees Performing) 3767.8.2 Input Wizard Example for Social Cloud (200 Employees Performing) 3797.9 Stochastic Cloud System Simulation 3797.9.1 Introduction and Methodology 3817.9.2 Numerical Applications for Ss to Verify Non-Ss 3857.9.3 Details of Probability Distributions Used in Stochastic Simulation 3877.9.4 Varying Product Repair and Failure Date with Empirical Bayesian Posterior Gamma Approach 3937.9.5 Varying Link Repair and Failure Using Gamma Distribution 3937.9.6 Ss Applied to a Power or Cyber Grid 3947.9.7 Error Checking or Flagging 3967.10 Cloud Risk Meter Analysis 3977.10.1 Risk Assessment and Management Clarifications for Figures 7.72 and 7.73 4027.11 Discussions and Conclusion 4057.12 Exercises 407References 4168 Software Reliability Modeling and Metrics in Cyber-Risk 4218.1 Introduction, Motivation, and Methodology 4218.2 History and Classification of Software Reliability Models 4228.2.1 Time-between-Failures Models 4228.2.2 Failure-Counting Models 4228.2.3 Bayesian Model 4238.2.4 Static (Nondynamic) Models 4238.2.5 Others 4248.3 Software Reliability Models in Time Domain 4248.4 Software Reliability Growth Models 4258.4.1 Negative Exponential Class of Failure Times 4258.4.2 J–M De-eutrophication Model (Binomial Type) 4258.4.3 Moranda’s Geometric Model (Poisson Type) 4268.4.4 Goel–Okumoto Nonhomogeneous Poisson Process (Poisson Type) 4278.4.5 Musa’s Basic Execution Time Model (Poisson Type) 4288.4.6 Musa–Okumoto Logarithmic Poisson Execution Time Model (Poisson Type) 4298.4.7 L–V Bayesian Model 4318.4.8 Sahinoglu’s Compound Poisson^Geometric and Poisson^Logarithmic Series Models 4338.4.9 Gamma, Weibull, and Other Classes of Failure Times 4358.4.10 Duane Model (Poisson Type) 4398.5 Numerical Examples Using Pedagogues 4408.5.1 Example 1 4408.5.2 Example 2 4418.6 Recent Trends in Software Reliability 4418.7 Discussions and Conclusion 4428.8 Exercises 444References 4459 Metrics for Software Reliability Failure-Count Models in Cyber-Risk 4519.1 Introduction and Methodology on Failure-Count Estimation in Software Reliability 4519.1.1 Statistical Estimation Models, Computational Formulas, and Examples 4529.1.2 Interpretations of Numerical Examples and Discussions 4649.2 Predictive Accuracy to Compare Failure-Count Models 4669.2.1 Classical Distribution Approach 4689.2.2 Prior Distribution Approach 4699.2.3 Applications to Data Sets and Comparisons 4729.3 Discussions and Conclusion 473appendix 9.A 4779.4 Exercises 478References 48210 Practical Hands-On Lab Topics in Cyber-Risk 48310.1 System Hardening 48310.1.1 General 48310.1.2 Windows Servers 48410.1.3 Wireless 48410.1.4 Firewalls, Routers, and Switches 48510.2 Email Security 48610.2.1 Identifying Fake Emails 48610.2.2 Emotion Responses 48610.3 MS-DOS Commands 48710.3.1 Mapping Intel 48810.4 Logging 49210.4.1 Policy 49310.4.2 Understanding Logs 49410.5 Firewall 49510.5.1 Traditional Firewalls 49510.5.2 Ngfs 49610.5.3 Host-Based Firewalls 49610.6 Wireless Networks 49610.7 Discussions and Conclusion 499Appendix 10.A 50010.8 Exercises 50110.8.1 System Hardening 50110.8.2 Email 50110.8.3 Ms-Dos 50210.8.4 Logging 50310.8.5 Firewall 50310.8.6 Wireless 50510.8.7 Comprehensive Exercises 50510.8.8 Cryptology Projects 507References 509What the Cyber-Risk Informatics Textbook and the Author are About? 511Index 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 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 xivReviews xvPreface xxiAcknowledgments and Dedication xxixAbout the Author xxxi1 Metrics, Statistical Quality Control, and Basic Reliability in Cyber-Risk 11.1 Deterministic and Stochastic Cyber-Risk Metrics 11.2 Statistical Risk Analysis 21.2.1 Introduction to Statistical Hypotheses 21.2.2 Decision Rules 31.2.3 One-Tailed Tests 41.2.4 Two-Tailed Tests 41.2.5 Decision Errors 61.2.6 Applications to One-Tailed Tests Associated with Both Type I and Type II Errors 71.2.7 Applications to Two-Tailed Tests (Normal Distribution Assumption) 111.3 Acceptance Sampling in Quality Control 161.3.1 Introduction 161.3.2 Definition of an Acceptance Sampling Plan 161.3.3 The OC Curve 161.4 Poisson and Normal Approximation to Binomial in Quality Control 191.4.1 Approximations to Binomial Distribution 191.4.2 Approximation of Binomial to Poisson Distribution 191.4.3 Approximation to Normal Distribution 201.4.4 Comparisons of Normal and Poisson Approximations to the Binomial 211.5 Basic Statistical Reliability Concepts and Mc Simulators 211.5.1 Fundamental Equations for Reliability, Hazard, and Statistical Notions 231.5.2 Fundamentals for Reliability Block Diagramming and Redundancy 271.5.3 Solving Basic Reliability Questions by Using Student-Friendly Pedagogical Examples 301.5.4 MC Simulators for Commonly Used Distributions in Reliability 471.6 Discussions and Conclusion 521.7 Exercises 52References 602 Complex Network Reliability Evaluation and Estimation in Cyber-Risk 612.1 Introduction 612.2 Overlap Technique to Calculate Complex Network Reliability 622.2.1 Network State Enumeration and Example 1 632.2.2 Generating Minimal Paths and Example 2 642.2.3 Overlap Method Algorithmic Rules and Example 3 682.3 The Overlap Method: Monte Carlo and Discrete Event Simulation 702.4 Multistate System Reliability Evaluation 712.4.1 Simple Series System with Single Derated States 732.4.2 Active Parallel System 732.4.3 Simple Series–Parallel System 742.4.4 A Simple Series–Parallel System with Multistate Components 752.4.5 A Combined System: Power Plant Example 762.4.6 Large Network Examples Using Multistate Overlap Technique 772.5 Weibull Time Distributed Reliability Evaluation 782.5.1 Motivation behind Weibull Probability Modeling 782.5.2 Weibull Parameter Estimation Methodology 792.5.3 Overlap Algorithm Applied to Weibull Distributed Components 802.5.4 Estimating Weibull Parameters 802.5.5 Fifty-Two-Node Weibull Example for Estimating Weibull Parameters 852.5.6 A Weibull Network Example from an Oil Rig System 902.6 Discussions and Conclusion 90Appendix 2.A Overlap Algorithm and Example 932.A.1 Algorithm 932.A.2 Example 952.7 Exercises 101References 1033 Stopping Rules for Reliability and Security Tests in Cyber-Risk 1053.1 Introduction 1053.2 Methods 1073.2.1 Lgm by Verhulst 1083.2.2 Compound Poisson Model 1103.3 Examples Merging Both Stopping Rules: Lgm and Cpm 1143.3.1 The DR5 Data Set Example 1143.3.2 The Dr4 Data Set Example 1183.3.3 The Supercomputing Cloud Historical Failure Data—Case Study 1193.3.4 Appendix for Section 3.3 1213.4 Stopping Rule for Testing in the Time Domain 1313.4.1 Review of Compound Poisson Process and Stopping Rule 1313.4.2 Empirical Bayes Analysis for the Poisson^Geometric Stopping Rule 1323.4.3 Howden’s Model for Stopping Rule 1353.4.4 Computational Example for Stopping-Rule Algorithm in Time Domain 1363.5 Discussions and Conclusion 1393.6 Exercises 143References 1444 Security Assessment and Management in Cyber-Risk 1474.1 Introduction 1474.1.1 What Other Scoring Methods Are Available? 1484.2 Security Meter (Sm) Model Design 1524.3 Verification of the Probabilistic Security Meter (Sm) Method by Monte Carlo Simulation and Math-Statistical Triple-Product Rule 1544.3.1 The Triple-Product Rule of Uniforms 1564.3.2 Data Analysis on the Total Residual Risk of the Security Meter Design 1584.3.3 Triple-Product Rule Discussions 1694.4 Modifying the SM Quantitative Model for Categorical, Hybrid, and Nondisjoint Data 1704.5 Maintenance Priority Determination for 3 × 3 × 2 Sm 1784.6 Privacy Meter (PM): How to Quantify Privacy Breach 1834.6.1 Methodology 1844.6.2 Privacy Risk-Meter Assessment and Management Examples 1854.7 Polish Decoding (Decompression) Algorithm 1874.8 Discussions and Conclusion 1894.9 Exercises 190References 1995 Game-Theoretic Computing in Cyber-Risk 2015.1 Historical Perspective to Game Theory’s Origins 2015.2 Applications of Game Theory to Cyber-Security Risk 2035.3 Intuitive Background: Concepts, Definitions, and Nomenclature 2045.3.1 A Price War Example 2055.4 Random Selection for Nash Mixed Strategy 2085.4.1 Random Probabilistic Selection 2085.4.2 Does Nash Equilibrium (NE) Exist for the Company A/B Problem in Table 5.1? 2095.4.3 An Example: Matching Pennies 2105.4.4 Another Game: The Prisoner’s Dilemma 2105.4.5 Games with Multiple NE (Terrorist Game: Bold Strategy Result in Domination) 2115.5 Adversarial Risk Analysis Models by Banks, Rios, and Rios 2135.6 An Alternative Model: Sahinoglu’s Security Meter for Neumann and Nash Mixed Strategy 2155.7 Other Interdisciplinary Applications of Risk Meters 2205.8 Mixed Strategy for Risk Assessment and Management-University Server and Social Network Examples 2215.8.1 University Server’s Security Risk-Meter Example 2215.8.2 Social Networks’ Privacy and Security Risk-Meter (RM) Example 2225.8.3 Clarification of Risk Assessment and Management Algorithm for Social Networks 2245.9 Application to Hospital Healthcare Service Risk 2265.10 Application to Environmetrics and Ecology Risk 2295.11 Application to Digital Forensics Security Risk 2345.12 Application to Business Contracting Risk 2395.13 Application to National Cybersecurity Risk 2455.14 Application to Airport Service Quality Risk 2535.15 Application to Offshore Oil-Drilling Spill and Security Risk 2575.16 Discussions and Conclusion 2645.17 Exercises 266References 2716 Modeling and Simulation in Cyber-Risk 2776.1 Introduction and a Brief History to Simulation 2776.2 Generic Theory: Case Studies on Goodness of Fit for Uniform Numbers 2786.3 Why Crucial to Manufacturing and Cyber Defense 2796.4 A Cross Section of Modeling and Simulation in Manufacturing Industry 2806.4.1 Modeling and Simulation of Multistate Production Units and Systems in Manufacturing 2816.4.2 Two-State SL Probability Model of Units with Closed-Form Solution 2836.4.3 Extended Three-State SL Probability Model of Up–Down –Derated Units with Mc Simulation 2846.4.4 Statistical Simulation of Three-State Units to Estimate the Density of Up–Down –Der 2896.4.5 How to Generate Random Numbers from Sl pdf to Simulate Component and System Behavior 2966.4.6 Example of Sl Simulation for Modeling Network of 2-in-Simple-Series Two-State (Up–Dn) Units 2976.4.7 Example of Sl Simulation for Modeling a Network of 7-in-Complex-Topology Two-State (Up–Dn) Units 3006.5 A Review of Modeling and Simulation in Cyber-Security 3016.5.1 MC Value-at-Risk Approach by Kim et al. in Cloud Computing 3016.5.2 MC and DES in Security Meter (Sm) Risk Model 3026.6 Application of Queuing Theory and Multichannel Simulation to Cyber-Security 3066.6.1 Example 1: One Recovery-Crew Case for Cyber-Security Queuing Simulation 3066.6.2 Example 2: Two Recovery-Crew Case for Cyber-Security Queuing Simulation 3086.7 Discussions and Conclusion 308Appendix 6.A 3116.8 Exercises 315References 3357 Cloud Computing in Cyber-Risk 3397.1 Introduction and Motivation 3397.2 Cloud Computing Risk Assessment 3427.3 Motivation and Methodology 3437.3.1 History of Theoretical Developments on CLOUD Modeling 3437.3.2 Notation 3447.3.3 Objectives 3447.3.4 Frequency and Duration Method for the Loss of Load or Service 3457.3.5 Nbd as a Compound Poisson Model 3467.3.6 Nbd for the Loss of Load or Loss of Cloud Service Expected 3487.4 Various Applications to Cyber Systems 3497.4.1 Small Sample Experimental Systems 3497.4.2 Large Cyber Systems 3537.5 Large Cyber Systems Using Statistical Methods 3577.6 Repair Crew and Product Reserve Planning to Manage Risk Cost Effectively Using Cyberrisksolver Cloud Management Java Tool 3597.6.1 Cloud Resource Management Planning for Employment of Repair Crews 3607.6.2 Cloud Resource Management Planning by Production Deployment 3657.7 Remarks for “Physical Cloud” Employing Physical Products (Servers, Generators, Communication Towers, Etc.) 3687.8 Applications to “Social (Human Resources) Cloud” 3727.8.1 Numerical Example for Social Cloud (200 Employees Performing) 3767.8.2 Input Wizard Example for Social Cloud (200 Employees Performing) 3797.9 Stochastic Cloud System Simulation 3797.9.1 Introduction and Methodology 3817.9.2 Numerical Applications for Ss to Verify Non-Ss 3857.9.3 Details of Probability Distributions Used in Stochastic Simulation 3877.9.4 Varying Product Repair and Failure Date with Empirical Bayesian Posterior Gamma Approach 3937.9.5 Varying Link Repair and Failure Using Gamma Distribution 3937.9.6 Ss Applied to a Power or Cyber Grid 3947.9.7 Error Checking or Flagging 3967.10 Cloud Risk Meter Analysis 3977.10.1 Risk Assessment and Management Clarifications for Figures 7.72 and 7.73 4027.11 Discussions and Conclusion 4057.12 Exercises 407References 4168 Software Reliability Modeling and Metrics in Cyber-Risk 4218.1 Introduction, Motivation, and Methodology 4218.2 History and Classification of Software Reliability Models 4228.2.1 Time-between-Failures Models 4228.2.2 Failure-Counting Models 4228.2.3 Bayesian Model 4238.2.4 Static (Nondynamic) Models 4238.2.5 Others 4248.3 Software Reliability Models in Time Domain 4248.4 Software Reliability Growth Models 4258.4.1 Negative Exponential Class of Failure Times 4258.4.2 J–M De-eutrophication Model (Binomial Type) 4258.4.3 Moranda’s Geometric Model (Poisson Type) 4268.4.4 Goel–Okumoto Nonhomogeneous Poisson Process (Poisson Type) 4278.4.5 Musa’s Basic Execution Time Model (Poisson Type) 4288.4.6 Musa–Okumoto Logarithmic Poisson Execution Time Model (Poisson Type) 4298.4.7 L–V Bayesian Model 4318.4.8 Sahinoglu’s Compound Poisson^Geometric and Poisson^Logarithmic Series Models 4338.4.9 Gamma, Weibull, and Other Classes of Failure Times 4358.4.10 Duane Model (Poisson Type) 4398.5 Numerical Examples Using Pedagogues 4408.5.1 Example 1 4408.5.2 Example 2 4418.6 Recent Trends in Software Reliability 4418.7 Discussions and Conclusion 4428.8 Exercises 444References 4459 Metrics for Software Reliability Failure-Count Models in Cyber-Risk 4519.1 Introduction and Methodology on Failure-Count Estimation in Software Reliability 4519.1.1 Statistical Estimation Models, Computational Formulas, and Examples 4529.1.2 Interpretations of Numerical Examples and Discussions 4649.2 Predictive Accuracy to Compare Failure-Count Models 4669.2.1 Classical Distribution Approach 4689.2.2 Prior Distribution Approach 4699.2.3 Applications to Data Sets and Comparisons 4729.3 Discussions and Conclusion 473appendix 9.A 4779.4 Exercises 478References 48210 Practical Hands-On Lab Topics in Cyber-Risk 48310.1 System Hardening 48310.1.1 General 48310.1.2 Windows Servers 48410.1.3 Wireless 48410.1.4 Firewalls, Routers, and Switches 48510.2 Email Security 48610.2.1 Identifying Fake Emails 48610.2.2 Emotion Responses 48610.3 MS-DOS Commands 48710.3.1 Mapping Intel 48810.4 Logging 49210.4.1 Policy 49310.4.2 Understanding Logs 49410.5 Firewall 49510.5.1 Traditional Firewalls 49510.5.2 Ngfs 49610.5.3 Host-Based Firewalls 49610.6 Wireless Networks 49610.7 Discussions and Conclusion 499Appendix 10.A 50010.8 Exercises 50110.8.1 System Hardening 50110.8.2 Email 50110.8.3 Ms-Dos 50210.8.4 Logging 50310.8.5 Firewall 50310.8.6 Wireless 50510.8.7 Comprehensive Exercises 50510.8.8 Cryptology Projects 507References 509What the Cyber-Risk Informatics Textbook and the Author are About? 511Index 513