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Book SynopsisPresents various challenges faced by security policy makers and risk analysts, and mathematical approaches that inform homeland security policy development and decision support Compiled by a group of highly qualified editors, this book provides a clear connection between risk science and homeland security policy making and includes top-notch contributions that uniquely highlight the role of risk analysis for informing homeland security policy decisions. Featuring discussions on various challenges faced in homeland security risk analysis, the book seamlessly divides the subject of risk analysis for homeland security into manageable chapters, which are organized by the concept of risk-informed decisions, methodology for applying risk analysis, and relevant examples and case studies. Applied Risk Analysis for Guiding Homeland Security Policy and Decisions offers an enlightening overview of risk analysis methods for homeland security. For instance, it presents readers with an exploration o
Table of ContentsAbout the Editors xix
List of Contributors xxi
Preface xxv
Chapter Abstracts xxviii
Part I Managing National Security Risk and Policy Programs 1
1 On the “Influence of Scenarios to Priorities” in Risk and Security Programs 3
Heimir Thorisson and James H. Lambert
1.1 Introduction 3
1.2 Risk Programs 4
1.3 Canonical Questions Guiding Development of Risk Programs 6
1.3.1 Canonical Question I: Scope 6
1.3.2 Canonical Question II: Operational Design 7
1.3.3 Canonical Question III: Evaluation 7
1.4 Scenario-Based Preferences 8
1.5 Methodology 9
1.6 Demonstration of Methods 12
1.7 Discussion and Conclusions 20
Acknowledgments 22
References 22
2 Survey of Risk Analytic Guidelines Across the Government 25
Isaac Maya, Amelia Liu, Lily Zhu, Francine Tran, Robert Creighton and CharlesWoo
2.1 Department of Defense (DOD) Overview 25
2.1.1 Joint Risk Analysis Methodology (JRAM) for the Chairman’s Risk Assessment (CRA) 26
2.1.2 Mission Assurance (MA): Risk Assessment and Management for DOD Missions 29
2.1.3 Risk Management Guide for DOD Acquisition 31
2.2 Department of Justice (DOJ) 33
2.3 Environmental Protection Agency (EPA) Overview 36
2.3.1 EPA Risk Leadership 36
2.3.2 EPA Risk Assessment Methodology and Guidelines 37
2.3.3 Risk Assessment Case Studies 40
2.3.4 Risk Assessment Challenges of EPA 43
2.3.5 Review of EPA Risk Assessment/Risk Management Methodologies 43
2.4 National Aeronautics and Space Administration (NASA): Overview 44
2.4.1 NASA Risk Leadership 44
2.4.2 Critical Steps in NASA Risk Assessment/Risk Management 44
2.4.3 Risk Assessment/Risk Management Challenges of NASA 48
2.4.4 Review of NASA Risk Assessment/Risk Management Methodologies 49
2.5 Nuclear Regulatory Commission (NRC) Overview 49
2.5.1 NRC Leadership 51
2.5.2 Critical Steps in NRC Risk Assessment/Risk Management 52
2.5.3 Risk Assessment/Risk Management Challenges of NRC 53
2.5.4 Review of NRC Risk Assessment/Risk Management Methodologies 54
2.6 International Standards Organization (ISO) Overview 55
2.6.1 ISO Leadership 57
2.6.2 Critical Steps in ISO Risk Assessment/Risk Management 57
2.6.3 Risk Assessment/Risk Management Challenges of ISO 58
2.7 Australia Overview 58
2.7.1 Australia Leadership 59
2.7.2 Critical Steps in Australia Risk Assessment/Risk Management 60
2.7.3 Risk Assessment/Risk Management Challenges of Australia 61
2.8 UK Overview 61
2.8.1 UK Leadership 61
2.8.2 Critical Steps in UK Risk Assessment/Risk Management 62
2.8.3 Risk Assessment/Risk Management Challenges of the United Kingdom 65
Acknowledgments 65
References 65
3 An Overview of Risk ModelingMethods and Approaches for National Security 69
Samrat Chatterjee, Robert T. Brigantic and Angela M.Waterworth
3.1 Introduction 69
3.2 Homeland Security Risk Landscape and Missions 70
3.2.1 Risk Landscape 71
3.2.2 Security Missions 71
3.2.3 Risk Definitions and Interpretations from DHS Risk Lexicon 72
3.3 Background Review 73
3.3.1 1960s to 1990s: Focus on Foundational Concepts 73
3.3.2 The 2000s: Increased Focus on Multi-hazard Risks Including Terrorism 75
3.3.3 2009 to Present: Emerging Emphasis on System Resilience and Complexity 78
3.4 Modeling Approaches for Risk Elements 88
3.4.1 Threat Modeling 88
3.4.2 VulnerabilityModeling 88
3.4.2.1 Survey-Based Methods 88
3.4.2.2 Systems Analysis 89
3.4.2.3 Network-Theoretic Approaches 89
3.4.2.4 Structural Analysis and ReliabilityTheory 89
3.4.3 Consequence Modeling 89
3.4.3.1 Direct Impacts 89
3.4.3.2 Indirect Impacts 89
3.4.4 Risk-Informed Decision Making 90
3.5 Modeling Perspectives for Further Research 90
3.5.1 Systemic Risk and ResilienceWithin a Unified Framework 90
3.5.2 Characterizing Cyber and Physical Infrastructure System Behaviors and Hazards 91
3.5.3 Utilizing “Big” Data or Lack of Data for Generating Risk and Resilience Analytics 91
3.5.4 Conceptual Multi-scale, Multi-hazard Modeling Framework 92
3.6 Concluding Remarks 94
Acknowledgments 95
References 95
4 Comparative Risk Rankings in Support of Homeland Security Strategic Plans 101
Russell Lundberg
4.1 Introduction 101
4.2 Conceptual Challenges in Comparative Risk Ranking 102
4.3 Practical Challenges in Comparative Ranking of Homeland Security Risks 103
4.3.1 Choosing a Risk Set 104
4.3.1.1 Lessons from the DMRR on Hazard Set Selection 105
4.3.2 Identifying Attributes to Consider 105
4.3.2.1 Lessons from the DMRR on Attribute Selection 107
4.3.3 Assessing Each Risk Individually 109
4.3.3.1 Lessons from the DMRR on Assessing Individual Homeland Security Risks 111
4.3.4 Combining Individual Risks to Develop a Comparative Risk Ranking 112
4.3.4.1 Lessons from the DMRR on Comparing Homeland Security Risks 114
4.4 Policy Relevance to Strategic-Level Homeland Security Risk Rankings 116
4.4.1 Insights into Homeland Security Risk Rankings 116
4.4.2 Risk vs. Risk Reduction 118
Acknowledgments 120
References 120
5 A Data ScienceWorkflow for Discovering Spatial Patterns Among Terrorist Attacks and Infrastructure 125
Daniel C. Fortin, Thomas Johansen, Samrat Chatterjee, GeorgeMuller and Christine Noonan
5.1 Introduction 125
5.2 The Data: Global Terrorism Database 126
5.3 The Tools: Exploring Data Interactively Using a Custom Shiny App 127
5.4 Example: Using the App to Explore ISIL Attacks 130
5.5 TheModels: StatisticalModels for Terrorist Event Data 134
5.6 More Data: Obtaining Regional Infrastructure Data to Build Statistical Models 135
5.7 A Model: Determining the Significance of Infrastructure on the Likelihood of an Attack 137
5.8 Case Study: Libya 138
5.9 Case Study: Jammu and Kashmir Region of India 139
5.9.1 The Model Revisited: Accounting for Many Regions with No Recorded Attacks 141
5.9.2 Investigating the Effect of Outliers 145
5.9.3 The Insight: What Have We Learned? 147
5.10 Summary 148
References 148
Part II Strengthening Ports of Entry 151
6 Effects of Credibility of Retaliation Threats in Deterring Smuggling of Nuclear Weapons 153
Xiaojun Shan and Jun Zhuang
6.1 Introduction 153
6.2 Extending Prior Game-Based Model 158
6.3 Comparing the Game Trees 158
6.4 The Extended Model 161
6.5 Solution to the Extended Model 162
6.6 Comparing the Solutions in Prior Game-Based Model and This Study 163
6.7 Illustration of the Extended Model Using Real Data 164
6.8 Conclusion and Future Research Work 165
References 167
7 Disutility of Mass Relocation After a Severe Nuclear Accident 171
VickiM. Bier and Shuji Liu
7.1 Introduction 171
7.2 Raw Data 174
7.3 Trade-Offs Between Cancer Fatalities and Relocation 177
7.4 Risk-Neutral DisutilityModel 179
7.5 Risk-Averse DisutilityModel 179
7.6 DisutilityModel with Interaction Effects 182
7.7 Economic Analysis 185
7.8 Conclusion 190
References 191
8 Scheduling Federal Air Marshals Under Uncertainty 193
KeithW. DeGregory and Rajesh Ganesan
8.1 Introduction 193
8.2 Literature 196
8.2.1 Commercial Aviation Industry 196
8.2.2 Homeland Security and the Federal Air Marshals Service 198
8.2.3 Approximate Dynamic Programming 199
8.3 Air Marshal Resource Allocation Model 200
8.3.1 Risk Model 200
8.3.2 Static Allocation 202
8.3.3 Dynamic Allocation 203
8.4 Stochastic Dynamic Programming Formulation 204
8.4.1 System State 205
8.4.2 Decision Variable 205
8.4.3 Post-decision State 206
8.4.4 Exogenous Information 206
8.4.5 State Transition Function 206
8.4.6 Contribution Function 206
8.4.7 Objective Function 207
8.4.8 Bellman’s Optimality Equations 207
8.5 Phases of Stochastic Dynamic Programming 207
8.5.1 Exploration Phase 207
8.5.2 Learning Phase 208
8.5.2.1 Algorithm 208
8.5.2.2 Approximation Methods 208
8.5.2.3 Convergence 209
8.5.3 Learned Phase 210
8.6 Integrated Allocation Model 210
8.7 Results 211
8.7.1 Experiment 211
8.7.2 Results from Stochastic Dynamic Programming Model 211
8.7.3 Sensitivity Analysis 212
8.7.4 Model Output 214
8.8 Conclusion 217
Acknowledgments 218
References 218
Part III Securing Critical Cyber Assets 221
9 Decision Theory for Network Security: Active Sensing for Detection and Prevention of Data Exfiltration 223
Sara M. McCarthy, Arunesh Sinha,Milind Tambe and Pratyusa Manadhatha
9.1 Introduction 223
9.1.1 Problem Domain 224
9.2 Background and RelatedWork 226
9.2.1 DNS Exfiltration 226
9.2.2 Partially Observable Markov Decision Process (POMDP) 228
9.3 Threat Model 229
9.3.1 The POMDP Model 230
9.4 POMDP Abstraction 232
9.4.1 Abstract Actions 232
9.4.2 Abstract Observations 234
9.4.3 VD-POMDP Factored Representation 234
9.4.4 Policy Execution 236
9.5 VD-POMDP Framework 239
9.6 Evaluation 241
9.6.1 Synthetic Networks 241
9.6.2 DETER Testbed Simulation 241
9.6.3 Runtime 242
9.6.4 Performance 244
9.6.5 Robustness 246
9.7 GameTheoretic Extensions 247
9.7.1 Threat Model 248
9.8 Conclusion and FutureWork 249
Acknowledgments 249
References 249
10 Measurement of Cyber Resilience from an Economic Perspective 253
Adam Z. Rose and NoahMiller
10.1 Introduction 253
10.2 Economic Resilience 254
10.2.1 Basic Concepts of Cyber Resilience 254
10.2.2 Basic Concepts of Economic Resilience 254
10.2.3 Economic Resilience Metrics 255
10.3 Cyber System Resilience Tactics 257
10.4 Resilience for Cyber-Related Sectors 267
10.4.1 Resilience in the Manufacturing of Cyber Equipment 267
10.4.2 Resilience in the Electricity Sector 268
10.5 Conclusion 269
References 270
11 Responses to Cyber Near-Misses: A Scale to Measure Individual Differences 275
Jinshu Cui, Heather Rosoff and Richard S. John
11.1 Introduction 275
11.2 Scale Development and Analysis Outline 277
11.3 Method 278
11.3.1 Measures 278
11.3.1.1 Cyber Near-Miss Appraisal Scale (CNMAS) 278
11.3.1.2 Measures of Discriminant Validity 281
11.3.1.3 Measure of Predictive Validity 281
11.3.1.4 Participants and Procedures 281
11.4 Results 284
11.4.1 Dimensionality and Reliability 284
11.4.2 Item Response Analysis 284
11.4.3 Differential Item Functioning (DIF) 287
11.4.4 Effects of Demographic Variables 289
11.4.5 Discriminant Validity 290
11.4.6 Predictive Validity 290
11.5 Discussion 291
Acknowledgments 292
References 292
Part IV Enhancing Disaster Preparedness and Infrastructure Resilience 295
12 An InteractiveWeb-Based Decision Support Systemfor Mass Dispensing, Emergency Preparedness, and Biosurveillance 297
Eva K. Lee, Ferdinand H. Pietz, Chien-Hung Chen and Yifan Liu
12.1 Introduction 297
12.2 System Architecture and Design 299
12.3 System Modules and Functionalities 301
12.3.1 Interactive User Experience 301
12.3.2 Geographical Boundaries 301
12.3.3 Network of Service, Locations, and Population Flow and Assignment 302
12.3.4 ZIP Code and Population Composition 304
12.3.5 Multimodality Dispensing and Public–Private Partnership 305
12.3.6 POD Layout Design and Resource Allocation 308
12.3.7 Radiological Module 309
12.3.8 Biosurveillance 309
12.3.9 Regional Information Sharing, Reverse Reporting, Tracking and Monitoring, and Resupply 310
12.3.10 Multilevel End-User Access 311
12.4 Biodefense, Pandemic Preparedness Planning, and Radiological and Large-Scale Disaster Relief Efforts 312
12.4.1 Biodefense Mass Dispensing Regional Planning 312
12.4.2 Real-Life Disaster Response Effort 315
12.4.2.1 RealOpt-Haiti© 315
12.4.2.2 RealOpt-Regional and RealOpt-CRC for Fukushima Daiichi Nuclear Disaster 316
12.4.2.3 RealOpt-ASSURE© 318
12.5 Challenges and Conclusions 319
Acknowledgments 321
References 321
13 Measuring Critical Infrastructure Risk, Protection, and Resilience in an All-Hazards Environment 325
Julia A. Phillips and Frédéric Petit
13.1 Introduction to Critical Infrastructure Risk Assessment 325
13.2 Motivation for Critical Infrastructure Risk Assessments 326
13.2.1 Unrest pre-September 2001 326
13.2.2 Post-911 Critical Infrastructure Protection and Resilience 326
13.3 Decision Analysis Methodologies for Creating Critical Infrastructure Risk Indicators 327
13.3.1 Decision Analysis 328
13.3.2 Illustrative Calculations for an Index: Buying a Car 328
13.4 An Application of Critical Infrastructure Protection, Consequence, and Resilience Assessment 331
13.4.1 Protection and Vulnerability 334
13.4.1.1 Physical Security 335
13.4.1.2 Security Management 335
13.4.1.3 Security Force 335
13.4.1.4 Information Sharing 337
13.4.1.5 Security Activity Background 338
13.4.2 Resilience 339
13.4.2.1 Preparedness 341
13.4.2.2 Mitigation Measures 341
13.4.2.3 Response Capabilities 342
13.4.2.4 Recovery Mechanisms 343
13.4.3 Consequences 343
13.4.3.1 Human Consequences 345
13.4.3.2 Economic Consequences 346
13.4.3.3 Government Mission/Public Health/Psychological Consequences 346
13.4.3.4 Cascading Impact Consequences 347
13.4.4 Risk Indices Comparison 349
13.5 Infrastructure Interdependencies 350
13.6 What’s Next for Critical Infrastructure Risk Assessments 352
References 354
14 Risk AnalysisMethods in Resilience Modeling: An Overview of Critical Infrastructure Applications 357
Hiba Baroud
14.1 Introduction 357
14.2 Background 358
14.2.1 Risk Analysis 358
14.2.2 Resilience 359
14.2.3 Critical Infrastructure Systems 360
14.3 Modeling the Resilience of Critical Infrastructure Systems 361
14.3.1 Resilience Models 361
14.3.1.1 Manufacturing 361
14.3.1.2 Communications 362
14.3.1.3 Dams, Levees, andWaterways 363
14.3.1.4 Defense 363
14.3.1.5 Emergency Services 363
14.3.1.6 Energy 363
14.3.1.7 Transportation 364
14.3.1.8 Water/Wastewater 364
14.3.2 Discussion 365
14.3.2.1 Economic Impact 365
14.3.2.2 Social Impact 367
14.3.2.3 Interdependencies 367
14.4 Assessing Risk in Resilience Models 368
14.4.1 Probabilistic Methods 368
14.4.2 UncertaintyModeling 369
14.4.3 Simulation-Based Approaches 369
14.4.4 Data-Driven Analytics 370
14.5 Opportunities and Challenges 370
14.5.1 Opportunities 370
14.5.2 Challenges 371
14.6 Concluding Remarks 372
References 373
15 Optimal Resource Allocation Model to Prevent, Prepare, and Respond to Multiple Disruptions, with Application to the Deepwater Horizon Oil Spill and Hurricane Katrina 381
Cameron A.MacKenzie and Amro Al Kazimi
15.1 Introduction 381
15.2 Model Development 383
15.2.1 Resource Allocation Model 383
15.2.2 Extension to Uncertain Parameters 385
15.3 Application: Deepwater Horizon and Hurricane Katrina 386
15.3.1 Parameter Estimation 386
15.3.1.1 Oil Spill Parameters 387
15.3.1.2 Hurricane Parameters 388
15.3.2 Base Case Results 391
15.3.3 Sensitivity Analysis on Economic Impacts 394
15.3.4 Model with Uncertain Effectiveness 395
15.4 Conclusions 397
References 398
16 Inoperability Input–Output Modeling of Electric Power Disruptions 405
Joost R. Santos, Sheree Ann Pagsuyoin and Christian Yip
16.1 Introduction 405
16.2 Risk Analysis of Natural and Man-Caused Electric Power Disruptions 407
16.3 Risk Management Insights for Disruptive Events 408
16.4 Modeling the Ripple Effects for Disruptive Events 411
16.5 Inoperability Input–Output Model 412
16.5.1 Model Parameters 412
16.5.2 Sector Inoperability 413
16.5.3 InterdependencyMatrix 413
16.5.4 Demand Perturbation 414
16.5.5 Economic Resilience 414
16.5.6 Economic Loss 415
16.6 Sample Electric Power Disruptions Scenario Analysis for the United States 416
16.7 Summary and Conclusions 421
References 422
17 Quantitative Assessment of Transportation Network Vulnerability with Dynamic Traffic Simulation Methods 427
Venkateswaran Shekar and Lance Fiondella
17.1 Introduction 427
17.2 Dynamic Transportation Network Vulnerability Assessment 429
17.3 Sources of Input for Dynamic Transportation Network Vulnerability Assessment 431
17.4 Illustrations 432
17.4.1 Example 1: Simple Network 432
17.4.2 Example II: University of Massachusetts Dartmouth Evacuation 437
17.5 Conclusion and Future Research 439
References 440
18 Infrastructure Monitoring for Health and Security 443
Prodyot K. Basu
18.1 Introduction 443
18.2 Data Acquisition 447
18.3 Sensors 447
18.3.1 Underlying Principles of Some of the Popular Sensors Listed in Table 18.1 451
18.3.1.1 Fiber Optics 451
18.3.1.2 VibratingWire 451
18.3.1.3 Piezoelectric Sensors 456
18.3.1.4 Piezoresistive Sensors 456
18.3.1.5 Laser Vibrometer 456
18.3.1.6 Acoustic Emission Sensing 457
18.3.1.7 GPS and GNSS 458
18.3.2 Selection of a Sensor 459
18.4 Capturing and Transmitting Signals 459
18.5 Energy Harvesting 461
18.6 Robotic IHM 462
18.7 Cyber-Physical Systems 464
18.8 Conclusions 464
References 465
19 Exploring Metaheuristic Approaches for Solving the Traveling Salesman Problem Applied to Emergency Planning and Response 467
Ramakrishna Tipireddy, Javier Rubio-Herrero, Samrat Chatterjee and Satish Chikkagoudar
19.1 The Traveling Salesman Problem 467
19.1.1 Definition 467
19.1.2 Computational Complexity 467
19.1.3 Solution Algorithms 468
19.1.4 Emergency Response Application 468
19.2 Emergency Planning and Response as a Traveling Salesman Problem 468
19.3 Metaheuristic Approaches 469
19.3.1 Simulated Annealing 470
19.3.1.1 Overview 470
19.3.1.2 Pseudocode 471
19.3.1.3 Case Study Results 473
19.3.2 Tabu Search 473
19.3.2.1 Overview 473
19.3.2.2 Pseudocode 474
19.3.2.3 Case Study Results 476
19.3.3 Genetic Algorithms 476
19.3.3.1 Overview 476
19.3.3.2 Pseudocode 478
19.3.3.3 Case Study Results 479
19.3.4 Ant Colony Optimization 479
19.3.4.1 Overview 479
19.3.4.2 Stochastic Solution Construction 480
19.3.4.3 Pheromone Update 480
19.3.4.4 Pseudocode 481
19.3.4.5 Case Study Results 481
19.4 Discussion 482
19.5 Concluding Remarks 482
References 484
Index 487
