Details

Applied Risk Analysis for Guiding Homeland Security Policy and Decisions

Wiley Series in Operations Research and Management Science 1. Aufl.

von: Samrat Chatterjee, Robert T. Brigantic, Angela M. Waterworth
CHF 115.00
Verlag:	Wiley
Format:	PDF
Veröffentl.:	09.02.2021
ISBN/EAN:	9781119287476
Sprache:	englisch
Anzahl Seiten:	528

In den Warenkorb

Als Gutschein

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.

Beschreibungen

Titelbeschreibung

Presents 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 of radiological and nuclear risk assessment, along with analysis of uncertainties in radiological and nuclear pathways. It covers the advances in risk analysis for border security, as well as for cyber security. Other topics covered include: strengthening points of entry; systems modeling for rapid containment and casualty mitigation; and disaster preparedness and critical infrastructure resilience. <ul> <li>Highlights how risk analysis helps in the decision-making process for homeland security policy</li> <li>Presents specific examples that detail how various risk analysis methods provide decision support for homeland security policy makers and risk analysts</li> <li>Describes numerous case studies from academic, government, and industrial perspectives that apply risk analysis methods for addressing challenges within the U.S. Department of Homeland Security (DHS)</li> <li>Offers detailed information regarding each of the five DHS missions: prevent terrorism and enhance security; secure and manage our borders; enforce and administer our immigration laws; safeguard and secure cyberspace; and strengthen national preparedness and resilience</li> <li>Discusses the various approaches and challenges faced in homeland risk analysis and identifies improvements and methodological advances that influenced DHS to adopt an increasingly risk-informed basis for decision-making</li> <li>Written by top educators and professionals who clearly illustrate the link between risk science and homeland security policy making </li> </ul> Applied Risk Analysis for Guiding Homeland Security Policy and Decisions is an excellent textbook and/or supplement for upper-undergraduate and graduate-level courses related to homeland security risk analysis. It will also be an extremely beneficial resource and reference for homeland security policy analysts, risk analysts, and policymakers from private and public sectors, as well as researchers, academics, and practitioners who utilize security risk analysis methods.

Inhaltsverzeichnis

About 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

Autorenportrait

SAMRAT CHATTERJEE, PhD, is Senior Operations Research/Data Scientist and the Decision Modeling and Optimization Team Lead in the Computing and Analytics Division within the National Security Directorate at Pacific Northwest National Laboratory (PNNL). He is also Affiliate Professor of Civil and Environmental Engineering with Northeastern University in Boston.ROBERT T. BRIGANTIC, PhD, is Chief Operations Research Scientist and the Statistical Modeling and Experimental Design Team Lead in the Computing and Analytics Division within the National Security Directorate at PNNL. He is also Adjunct Professor of Operations Research with the Carson College of Business at the Washington State University.ANGELA M. WATERWORTH, MS, is Senior Operations Research/Data Scientist in the Computing and Analytics Division within the National Security Directorate at PNNL.

Back cover copy

Country of final manufacture

Diese Produkte könnten Sie auch interessieren:

V poiskah istochnikov rosta. Uchebnye keysy na materialah rossiyskih kompaniy

von: Andrey Starkov, Alexander Lebedev, Daniil Muravsky, Pavel Voloshchuk, Vera Rebyazina, Olga Kusraeva, Alexander Pligin, Olga Mondrus, Alexander Rozhkov, Andrey Dubovskov, Denis Klimanov