{"product_id":"cyberrisk-informatics-9781119087519","title":"CyberRisk Informatics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eThis book provides a scientific modeling approach for conducting metrics-based quantitative risk assessments of cybersecurity vulnerabilities and threats.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e En\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePrologue xiv\u003c\/p\u003e \u003cp\u003eReviews xv\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eAcknowledgments and Dedication xxix\u003c\/p\u003e \u003cp\u003eAbout the Author xxxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Metrics, Statistical Quality Control, and Basic Reliability in Cyber-Risk 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Deterministic and Stochastic Cyber-Risk Metrics 1\u003c\/p\u003e \u003cp\u003e1.2 Statistical Risk Analysis 2\u003c\/p\u003e \u003cp\u003e1.2.1 Introduction to Statistical Hypotheses 2\u003c\/p\u003e \u003cp\u003e1.2.2 Decision Rules 3\u003c\/p\u003e \u003cp\u003e1.2.3 One-Tailed Tests 4\u003c\/p\u003e \u003cp\u003e1.2.4 Two-Tailed Tests 4\u003c\/p\u003e \u003cp\u003e1.2.5 Decision Errors 6\u003c\/p\u003e \u003cp\u003e1.2.6 Applications to One-Tailed Tests Associated with Both Type I and Type II Errors 7\u003c\/p\u003e \u003cp\u003e1.2.7 Applications to Two-Tailed Tests (Normal Distribution Assumption) 11\u003c\/p\u003e \u003cp\u003e1.3 Acceptance Sampling in Quality Control 16\u003c\/p\u003e \u003cp\u003e1.3.1 Introduction 16\u003c\/p\u003e \u003cp\u003e1.3.2 Definition of an Acceptance Sampling Plan 16\u003c\/p\u003e \u003cp\u003e1.3.3 The OC Curve 16\u003c\/p\u003e \u003cp\u003e1.4 Poisson and Normal Approximation to Binomial in Quality Control 19\u003c\/p\u003e \u003cp\u003e1.4.1 Approximations to Binomial Distribution 19\u003c\/p\u003e \u003cp\u003e1.4.2 Approximation of Binomial to Poisson Distribution 19\u003c\/p\u003e \u003cp\u003e1.4.3 Approximation to Normal Distribution 20\u003c\/p\u003e \u003cp\u003e1.4.4 Comparisons of Normal and Poisson Approximations to the Binomial 21\u003c\/p\u003e \u003cp\u003e1.5 Basic Statistical Reliability Concepts and Mc Simulators 21\u003c\/p\u003e \u003cp\u003e1.5.1 Fundamental Equations for Reliability, Hazard, and Statistical Notions 23\u003c\/p\u003e \u003cp\u003e1.5.2 Fundamentals for Reliability Block Diagramming and Redundancy 27\u003c\/p\u003e \u003cp\u003e1.5.3 Solving Basic Reliability Questions by Using Student-Friendly Pedagogical Examples 30\u003c\/p\u003e \u003cp\u003e1.5.4 MC Simulators for Commonly Used Distributions in Reliability 47\u003c\/p\u003e \u003cp\u003e1.6 Discussions and Conclusion 52\u003c\/p\u003e \u003cp\u003e1.7 Exercises 52\u003c\/p\u003e \u003cp\u003eReferences 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Complex Network Reliability Evaluation and Estimation in Cyber-Risk 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 61\u003c\/p\u003e \u003cp\u003e2.2 Overlap Technique to Calculate Complex Network Reliability 62\u003c\/p\u003e \u003cp\u003e2.2.1 Network State Enumeration and Example 1 63\u003c\/p\u003e \u003cp\u003e2.2.2 Generating Minimal Paths and Example 2 64\u003c\/p\u003e \u003cp\u003e2.2.3 Overlap Method Algorithmic Rules and Example 3 68\u003c\/p\u003e \u003cp\u003e2.3 The Overlap Method: Monte Carlo and Discrete Event Simulation 70\u003c\/p\u003e \u003cp\u003e2.4 Multistate System Reliability Evaluation 71\u003c\/p\u003e \u003cp\u003e2.4.1 Simple Series System with Single Derated States 73\u003c\/p\u003e \u003cp\u003e2.4.2 Active Parallel System 73\u003c\/p\u003e \u003cp\u003e2.4.3 Simple Series–Parallel System 74\u003c\/p\u003e \u003cp\u003e2.4.4 A Simple Series–Parallel System with Multistate Components 75\u003c\/p\u003e \u003cp\u003e2.4.5 A Combined System: Power Plant Example 76\u003c\/p\u003e \u003cp\u003e2.4.6 Large Network Examples Using Multistate Overlap Technique 77\u003c\/p\u003e \u003cp\u003e2.5 Weibull Time Distributed Reliability Evaluation 78\u003c\/p\u003e \u003cp\u003e2.5.1 Motivation behind Weibull Probability Modeling 78\u003c\/p\u003e \u003cp\u003e2.5.2 Weibull Parameter Estimation Methodology 79\u003c\/p\u003e \u003cp\u003e2.5.3 Overlap Algorithm Applied to Weibull Distributed Components 80\u003c\/p\u003e \u003cp\u003e2.5.4 Estimating Weibull Parameters 80\u003c\/p\u003e \u003cp\u003e2.5.5 Fifty-Two-Node Weibull Example for Estimating Weibull Parameters 85\u003c\/p\u003e \u003cp\u003e2.5.6 A Weibull Network Example from an Oil Rig System 90\u003c\/p\u003e \u003cp\u003e2.6 Discussions and Conclusion 90\u003c\/p\u003e \u003cp\u003eAppendix 2.A Overlap Algorithm and Example 93\u003c\/p\u003e \u003cp\u003e2.A.1 Algorithm 93\u003c\/p\u003e \u003cp\u003e2.A.2 Example 95\u003c\/p\u003e \u003cp\u003e2.7 Exercises 101\u003c\/p\u003e \u003cp\u003eReferences 103\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Stopping Rules for Reliability and Security Tests in Cyber-Risk 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 105\u003c\/p\u003e \u003cp\u003e3.2 Methods 107\u003c\/p\u003e \u003cp\u003e3.2.1 Lgm by Verhulst 108\u003c\/p\u003e \u003cp\u003e3.2.2 Compound Poisson Model 110\u003c\/p\u003e \u003cp\u003e3.3 Examples Merging Both Stopping Rules: Lgm and Cpm 114\u003c\/p\u003e \u003cp\u003e3.3.1 The DR5 Data Set Example 114\u003c\/p\u003e \u003cp\u003e3.3.2 The Dr4 Data Set Example 118\u003c\/p\u003e \u003cp\u003e3.3.3 The Supercomputing Cloud Historical Failure Data—Case Study 119\u003c\/p\u003e \u003cp\u003e3.3.4 Appendix for Section 3.3 121\u003c\/p\u003e \u003cp\u003e3.4 Stopping Rule for Testing in the Time Domain 131\u003c\/p\u003e \u003cp\u003e3.4.1 Review of Compound Poisson Process and Stopping Rule 131\u003c\/p\u003e \u003cp\u003e3.4.2 Empirical Bayes Analysis for the Poisson^Geometric Stopping Rule 132\u003c\/p\u003e \u003cp\u003e3.4.3 Howden’s Model for Stopping Rule 135\u003c\/p\u003e \u003cp\u003e3.4.4 Computational Example for Stopping-Rule Algorithm in Time Domain 136\u003c\/p\u003e \u003cp\u003e3.5 Discussions and Conclusion 139\u003c\/p\u003e \u003cp\u003e3.6 Exercises 143\u003c\/p\u003e \u003cp\u003eReferences 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Security Assessment and Management in Cyber-Risk 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 147\u003c\/p\u003e \u003cp\u003e4.1.1 What Other Scoring Methods Are Available? 148\u003c\/p\u003e \u003cp\u003e4.2 Security Meter (Sm) Model Design 152\u003c\/p\u003e \u003cp\u003e4.3 Verification of the Probabilistic Security Meter (Sm) Method by Monte Carlo Simulation and Math-Statistical Triple-Product Rule 154\u003c\/p\u003e \u003cp\u003e4.3.1 The Triple-Product Rule of Uniforms 156\u003c\/p\u003e \u003cp\u003e4.3.2 Data Analysis on the Total Residual Risk of the Security Meter Design 158\u003c\/p\u003e \u003cp\u003e4.3.3 Triple-Product Rule Discussions 169\u003c\/p\u003e \u003cp\u003e4.4 Modifying the SM Quantitative Model for Categorical, Hybrid, and Nondisjoint Data 170\u003c\/p\u003e \u003cp\u003e4.5 Maintenance Priority Determination for 3 × 3 × 2 Sm 178\u003c\/p\u003e \u003cp\u003e4.6 Privacy Meter (PM): How to Quantify Privacy Breach 183\u003c\/p\u003e \u003cp\u003e4.6.1 Methodology 184\u003c\/p\u003e \u003cp\u003e4.6.2 Privacy Risk-Meter Assessment and Management Examples 185\u003c\/p\u003e \u003cp\u003e4.7 Polish Decoding (Decompression) Algorithm 187\u003c\/p\u003e \u003cp\u003e4.8 Discussions and Conclusion 189\u003c\/p\u003e \u003cp\u003e4.9 Exercises 190\u003c\/p\u003e \u003cp\u003eReferences 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Game-Theoretic Computing in Cyber-Risk 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Historical Perspective to Game Theory’s Origins 201\u003c\/p\u003e \u003cp\u003e5.2 Applications of Game Theory to Cyber-Security Risk 203\u003c\/p\u003e \u003cp\u003e5.3 Intuitive Background: Concepts, Definitions, and Nomenclature 204\u003c\/p\u003e \u003cp\u003e5.3.1 A Price War Example 205\u003c\/p\u003e \u003cp\u003e5.4 Random Selection for Nash Mixed Strategy 208\u003c\/p\u003e \u003cp\u003e5.4.1 Random Probabilistic Selection 208\u003c\/p\u003e \u003cp\u003e5.4.2 Does Nash Equilibrium (NE) Exist for the Company A\/B Problem in Table 5.1? 209\u003c\/p\u003e \u003cp\u003e5.4.3 An Example: Matching Pennies 210\u003c\/p\u003e \u003cp\u003e5.4.4 Another Game: The Prisoner’s Dilemma 210\u003c\/p\u003e \u003cp\u003e5.4.5 Games with Multiple NE (Terrorist Game: Bold Strategy Result in Domination) 211\u003c\/p\u003e \u003cp\u003e5.5 Adversarial Risk Analysis Models by Banks, Rios, and Rios 213\u003c\/p\u003e \u003cp\u003e5.6 An Alternative Model: Sahinoglu’s Security Meter for Neumann and Nash Mixed Strategy 215\u003c\/p\u003e \u003cp\u003e5.7 Other Interdisciplinary Applications of Risk Meters 220\u003c\/p\u003e \u003cp\u003e5.8 Mixed Strategy for Risk Assessment and Management-University Server and Social Network Examples 221\u003c\/p\u003e \u003cp\u003e5.8.1 University Server’s Security Risk-Meter Example 221\u003c\/p\u003e \u003cp\u003e5.8.2 Social Networks’ Privacy and Security Risk-Meter (RM) Example 222\u003c\/p\u003e \u003cp\u003e5.8.3 Clarification of Risk Assessment and Management Algorithm for Social Networks 224\u003c\/p\u003e \u003cp\u003e5.9 Application to Hospital Healthcare Service Risk 226\u003c\/p\u003e \u003cp\u003e5.10 Application to Environmetrics and Ecology Risk 229\u003c\/p\u003e \u003cp\u003e5.11 Application to Digital Forensics Security Risk 234\u003c\/p\u003e \u003cp\u003e5.12 Application to Business Contracting Risk 239\u003c\/p\u003e \u003cp\u003e5.13 Application to National Cybersecurity Risk 245\u003c\/p\u003e \u003cp\u003e5.14 Application to Airport Service Quality Risk 253\u003c\/p\u003e \u003cp\u003e5.15 Application to Offshore Oil-Drilling Spill and Security Risk 257\u003c\/p\u003e \u003cp\u003e5.16 Discussions and Conclusion 264\u003c\/p\u003e \u003cp\u003e5.17 Exercises 266\u003c\/p\u003e \u003cp\u003eReferences 271\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Modeling and Simulation in Cyber-Risk 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction and a Brief History to Simulation 277\u003c\/p\u003e \u003cp\u003e6.2 Generic Theory: Case Studies on Goodness of Fit for Uniform Numbers 278\u003c\/p\u003e \u003cp\u003e6.3 Why Crucial to Manufacturing and Cyber Defense 279\u003c\/p\u003e \u003cp\u003e6.4 A Cross Section of Modeling and Simulation in Manufacturing Industry 280\u003c\/p\u003e \u003cp\u003e6.4.1 Modeling and Simulation of Multistate Production Units and Systems in Manufacturing 281\u003c\/p\u003e \u003cp\u003e6.4.2 Two-State SL Probability Model of Units with Closed-Form Solution 283\u003c\/p\u003e \u003cp\u003e6.4.3 Extended Three-State SL Probability Model of Up–Down –Derated Units with Mc Simulation 284\u003c\/p\u003e \u003cp\u003e6.4.4 Statistical Simulation of Three-State Units to Estimate the Density of Up–Down –Der 289\u003c\/p\u003e \u003cp\u003e6.4.5 How to Generate Random Numbers from Sl pdf to Simulate Component and System Behavior 296\u003c\/p\u003e \u003cp\u003e6.4.6 Example of Sl Simulation for Modeling Network of 2-in-Simple-Series Two-State (Up–Dn) Units 297\u003c\/p\u003e \u003cp\u003e6.4.7 Example of Sl Simulation for Modeling a Network of 7-in-Complex-Topology Two-State (Up–Dn) Units 300\u003c\/p\u003e \u003cp\u003e6.5 A Review of Modeling and Simulation in Cyber-Security 301\u003c\/p\u003e \u003cp\u003e6.5.1 MC Value-at-Risk Approach by Kim et al. in Cloud Computing 301\u003c\/p\u003e \u003cp\u003e6.5.2 MC and DES in Security Meter (Sm) Risk Model 302\u003c\/p\u003e \u003cp\u003e6.6 Application of Queuing Theory and Multichannel Simulation to Cyber-Security 306\u003c\/p\u003e \u003cp\u003e6.6.1 Example 1: One Recovery-Crew Case for Cyber-Security Queuing Simulation 306\u003c\/p\u003e \u003cp\u003e6.6.2 Example 2: Two Recovery-Crew Case for Cyber-Security Queuing Simulation 308\u003c\/p\u003e \u003cp\u003e6.7 Discussions and Conclusion 308\u003c\/p\u003e \u003cp\u003eAppendix 6.A 311\u003c\/p\u003e \u003cp\u003e6.8 Exercises 315\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Cloud Computing in Cyber-Risk 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction and Motivation 339\u003c\/p\u003e \u003cp\u003e7.2 Cloud Computing Risk Assessment 342\u003c\/p\u003e \u003cp\u003e7.3 Motivation and Methodology 343\u003c\/p\u003e \u003cp\u003e7.3.1 History of Theoretical Developments on CLOUD Modeling 343\u003c\/p\u003e \u003cp\u003e7.3.2 Notation 344\u003c\/p\u003e \u003cp\u003e7.3.3 Objectives 344\u003c\/p\u003e \u003cp\u003e7.3.4 Frequency and Duration Method for the Loss of Load or Service 345\u003c\/p\u003e \u003cp\u003e7.3.5 Nbd as a Compound Poisson Model 346\u003c\/p\u003e \u003cp\u003e7.3.6 Nbd for the Loss of Load or Loss of Cloud Service Expected 348\u003c\/p\u003e \u003cp\u003e7.4 Various Applications to Cyber Systems 349\u003c\/p\u003e \u003cp\u003e7.4.1 Small Sample Experimental Systems 349\u003c\/p\u003e \u003cp\u003e7.4.2 Large Cyber Systems 353\u003c\/p\u003e \u003cp\u003e7.5 Large Cyber Systems Using Statistical Methods 357\u003c\/p\u003e \u003cp\u003e7.6 Repair Crew and Product Reserve Planning to Manage Risk Cost Effectively Using Cyberrisksolver Cloud Management Java Tool 359\u003c\/p\u003e \u003cp\u003e7.6.1 Cloud Resource Management Planning for Employment of Repair Crews 360\u003c\/p\u003e \u003cp\u003e7.6.2 Cloud Resource Management Planning by Production Deployment 365\u003c\/p\u003e \u003cp\u003e7.7 Remarks for “Physical Cloud” Employing Physical Products (Servers, Generators, Communication Towers, Etc.) 368\u003c\/p\u003e \u003cp\u003e7.8 Applications to “Social (Human Resources) Cloud” 372\u003c\/p\u003e \u003cp\u003e7.8.1 Numerical Example for Social Cloud (200 Employees Performing) 376\u003c\/p\u003e \u003cp\u003e7.8.2 Input Wizard Example for Social Cloud (200 Employees Performing) 379\u003c\/p\u003e \u003cp\u003e7.9 Stochastic Cloud System Simulation 379\u003c\/p\u003e \u003cp\u003e7.9.1 Introduction and Methodology 381\u003c\/p\u003e \u003cp\u003e7.9.2 Numerical Applications for Ss to Verify Non-Ss 385\u003c\/p\u003e \u003cp\u003e7.9.3 Details of Probability Distributions Used in Stochastic Simulation 387\u003c\/p\u003e \u003cp\u003e7.9.4 Varying Product Repair and Failure Date with Empirical Bayesian Posterior Gamma Approach 393\u003c\/p\u003e \u003cp\u003e7.9.5 Varying Link Repair and Failure Using Gamma Distribution 393\u003c\/p\u003e \u003cp\u003e7.9.6 Ss Applied to a Power or Cyber Grid 394\u003c\/p\u003e \u003cp\u003e7.9.7 Error Checking or Flagging 396\u003c\/p\u003e \u003cp\u003e7.10 Cloud Risk Meter Analysis 397\u003c\/p\u003e \u003cp\u003e7.10.1 Risk Assessment and Management Clarifications for Figures 7.72 and 7.73 402\u003c\/p\u003e \u003cp\u003e7.11 Discussions and Conclusion 405\u003c\/p\u003e \u003cp\u003e7.12 Exercises 407\u003c\/p\u003e \u003cp\u003eReferences 416\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Software Reliability Modeling and Metrics in Cyber-Risk 421\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction, Motivation, and Methodology 421\u003c\/p\u003e \u003cp\u003e8.2 History and Classification of Software Reliability Models 422\u003c\/p\u003e \u003cp\u003e8.2.1 Time-between-Failures Models 422\u003c\/p\u003e \u003cp\u003e8.2.2 Failure-Counting Models 422\u003c\/p\u003e \u003cp\u003e8.2.3 Bayesian Model 423\u003c\/p\u003e \u003cp\u003e8.2.4 Static (Nondynamic) Models 423\u003c\/p\u003e \u003cp\u003e8.2.5 Others 424\u003c\/p\u003e \u003cp\u003e8.3 Software Reliability Models in Time Domain 424\u003c\/p\u003e \u003cp\u003e8.4 Software Reliability Growth Models 425\u003c\/p\u003e \u003cp\u003e8.4.1 Negative Exponential Class of Failure Times 425\u003c\/p\u003e \u003cp\u003e8.4.2 J–M De-eutrophication Model (Binomial Type) 425\u003c\/p\u003e \u003cp\u003e8.4.3 Moranda’s Geometric Model (Poisson Type) 426\u003c\/p\u003e \u003cp\u003e8.4.4 Goel–Okumoto Nonhomogeneous Poisson Process (Poisson Type) 427\u003c\/p\u003e \u003cp\u003e8.4.5 Musa’s Basic Execution Time Model (Poisson Type) 428\u003c\/p\u003e \u003cp\u003e8.4.6 Musa–Okumoto Logarithmic Poisson Execution Time Model (Poisson Type) 429\u003c\/p\u003e \u003cp\u003e8.4.7 L–V Bayesian Model 431\u003c\/p\u003e \u003cp\u003e8.4.8 Sahinoglu’s Compound Poisson^Geometric and Poisson^Logarithmic Series Models 433\u003c\/p\u003e \u003cp\u003e8.4.9 Gamma, Weibull, and Other Classes of Failure Times 435\u003c\/p\u003e \u003cp\u003e8.4.10 Duane Model (Poisson Type) 439\u003c\/p\u003e \u003cp\u003e8.5 Numerical Examples Using Pedagogues 440\u003c\/p\u003e \u003cp\u003e8.5.1 Example 1 440\u003c\/p\u003e \u003cp\u003e8.5.2 Example 2 441\u003c\/p\u003e \u003cp\u003e8.6 Recent Trends in Software Reliability 441\u003c\/p\u003e \u003cp\u003e8.7 Discussions and Conclusion 442\u003c\/p\u003e \u003cp\u003e8.8 Exercises 444\u003c\/p\u003e \u003cp\u003eReferences 445\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Metrics for Software Reliability Failure-Count Models in Cyber-Risk 451\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction and Methodology on Failure-Count Estimation in Software Reliability 451\u003c\/p\u003e \u003cp\u003e9.1.1 Statistical Estimation Models, Computational Formulas, and Examples 452\u003c\/p\u003e \u003cp\u003e9.1.2 Interpretations of Numerical Examples and Discussions 464\u003c\/p\u003e \u003cp\u003e9.2 Predictive Accuracy to Compare Failure-Count Models 466\u003c\/p\u003e \u003cp\u003e9.2.1 Classical Distribution Approach 468\u003c\/p\u003e \u003cp\u003e9.2.2 Prior Distribution Approach 469\u003c\/p\u003e \u003cp\u003e9.2.3 Applications to Data Sets and Comparisons 472\u003c\/p\u003e \u003cp\u003e9.3 Discussions and Conclusion 473\u003c\/p\u003e \u003cp\u003eappendix 9.A 477\u003c\/p\u003e \u003cp\u003e9.4 Exercises 478\u003c\/p\u003e \u003cp\u003eReferences 482\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Practical Hands-On Lab Topics in Cyber-Risk 483\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 System Hardening 483\u003c\/p\u003e \u003cp\u003e10.1.1 General 483\u003c\/p\u003e \u003cp\u003e10.1.2 Windows Servers 484\u003c\/p\u003e \u003cp\u003e10.1.3 Wireless 484\u003c\/p\u003e \u003cp\u003e10.1.4 Firewalls, Routers, and Switches 485\u003c\/p\u003e \u003cp\u003e10.2 Email Security 486\u003c\/p\u003e \u003cp\u003e10.2.1 Identifying Fake Emails 486\u003c\/p\u003e \u003cp\u003e10.2.2 Emotion Responses 486\u003c\/p\u003e \u003cp\u003e10.3 MS-DOS Commands 487\u003c\/p\u003e \u003cp\u003e10.3.1 Mapping Intel 488\u003c\/p\u003e \u003cp\u003e10.4 Logging 492\u003c\/p\u003e \u003cp\u003e10.4.1 Policy 493\u003c\/p\u003e \u003cp\u003e10.4.2 Understanding Logs 494\u003c\/p\u003e \u003cp\u003e10.5 Firewall 495\u003c\/p\u003e \u003cp\u003e10.5.1 Traditional Firewalls 495\u003c\/p\u003e \u003cp\u003e10.5.2 Ngfs 496\u003c\/p\u003e \u003cp\u003e10.5.3 Host-Based Firewalls 496\u003c\/p\u003e \u003cp\u003e10.6 Wireless Networks 496\u003c\/p\u003e \u003cp\u003e10.7 Discussions and Conclusion 499\u003c\/p\u003e \u003cp\u003eAppendix 10.A 500\u003c\/p\u003e \u003cp\u003e10.8 Exercises 501\u003c\/p\u003e \u003cp\u003e10.8.1 System Hardening 501\u003c\/p\u003e \u003cp\u003e10.8.2 Email 501\u003c\/p\u003e \u003cp\u003e10.8.3 Ms-Dos 502\u003c\/p\u003e \u003cp\u003e10.8.4 Logging 503\u003c\/p\u003e \u003cp\u003e10.8.5 Firewall 503\u003c\/p\u003e \u003cp\u003e10.8.6 Wireless 505\u003c\/p\u003e \u003cp\u003e10.8.7 Comprehensive Exercises 505\u003c\/p\u003e \u003cp\u003e10.8.8 Cryptology Projects 507\u003c\/p\u003e \u003cp\u003eReferences 509\u003c\/p\u003e \u003cp\u003eWhat the Cyber-Risk Informatics Textbook and the Author are About? 511\u003c\/p\u003e \u003cp\u003eIndex 513\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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