{"product_id":"applied-risk-analysis-for-guiding-homeland-security-policy-and-decisions-isbn-9781119287469","title":"Applied Risk Analysis for Guiding Homeland Security Policy and Decisions","description":"\u003cp\u003e\u003cb\u003ePresents various challenges faced by security policy makers and risk analysts, and mathematical approaches that inform homeland security policy development and decision support\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCompiled 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. \u003c\/p\u003e \u003cp\u003e\u003ci\u003eApplied Risk Analysis for Guiding Homeland Security Policy and Decisions\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eHighlights how risk analysis helps in the decision-making process for homeland security policy\u003c\/li\u003e \u003cli\u003ePresents specific examples that detail how various risk analysis methods provide decision support for homeland security policy makers and risk analysts\u003c\/li\u003e \u003cli\u003eDescribes 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)\u003c\/li\u003e \u003cli\u003eOffers 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\u003c\/li\u003e \u003cli\u003eDiscusses 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\u003c\/li\u003e \u003cli\u003eWritten by top educators and professionals who clearly illustrate the link between risk science and homeland security policy making \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Risk Analysis for Guiding Homeland Security Policy and Decisions\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eAbout the Editors xix\u003c\/p\u003e \u003cp\u003eList of Contributors xxi\u003c\/p\u003e \u003cp\u003ePreface xxv\u003c\/p\u003e \u003cp\u003eChapter Abstracts xxviii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Managing National Security Risk and Policy Programs \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 On the “Influence of Scenarios to Priorities” in Risk and Security Programs \u003c\/b\u003e\u003cb\u003e3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHeimir Thorisson and James H. Lambert\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Risk Programs 4\u003c\/p\u003e \u003cp\u003e1.3 Canonical Questions Guiding Development of Risk Programs 6\u003c\/p\u003e \u003cp\u003e1.3.1 Canonical Question I: Scope 6\u003c\/p\u003e \u003cp\u003e1.3.2 Canonical Question II: Operational Design 7\u003c\/p\u003e \u003cp\u003e1.3.3 Canonical Question III: Evaluation 7\u003c\/p\u003e \u003cp\u003e1.4 Scenario-Based Preferences 8\u003c\/p\u003e \u003cp\u003e1.5 Methodology 9\u003c\/p\u003e \u003cp\u003e1.6 Demonstration of Methods 12\u003c\/p\u003e \u003cp\u003e1.7 Discussion and Conclusions 20\u003c\/p\u003e \u003cp\u003eAcknowledgments 22\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Survey of Risk Analytic Guidelines Across the Government \u003c\/b\u003e\u003cb\u003e25\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIsaac Maya, Amelia Liu, Lily Zhu, Francine Tran, Robert Creighton and CharlesWoo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Department of Defense (DOD) Overview 25\u003c\/p\u003e \u003cp\u003e2.1.1 Joint Risk Analysis Methodology (JRAM) for the Chairman’s Risk Assessment (CRA) 26\u003c\/p\u003e \u003cp\u003e2.1.2 Mission Assurance (MA): Risk Assessment and Management for DOD Missions 29\u003c\/p\u003e \u003cp\u003e2.1.3 Risk Management Guide for DOD Acquisition 31\u003c\/p\u003e \u003cp\u003e2.2 Department of Justice (DOJ) 33\u003c\/p\u003e \u003cp\u003e2.3 Environmental Protection Agency (EPA) Overview 36\u003c\/p\u003e \u003cp\u003e2.3.1 EPA Risk Leadership 36\u003c\/p\u003e \u003cp\u003e2.3.2 EPA Risk Assessment Methodology and Guidelines 37\u003c\/p\u003e \u003cp\u003e2.3.3 Risk Assessment Case Studies 40\u003c\/p\u003e \u003cp\u003e2.3.4 Risk Assessment Challenges of EPA 43\u003c\/p\u003e \u003cp\u003e2.3.5 Review of EPA Risk Assessment\/Risk Management Methodologies 43\u003c\/p\u003e \u003cp\u003e2.4 National Aeronautics and Space Administration (NASA): Overview 44\u003c\/p\u003e \u003cp\u003e2.4.1 NASA Risk Leadership 44\u003c\/p\u003e \u003cp\u003e2.4.2 Critical Steps in NASA Risk Assessment\/Risk Management 44\u003c\/p\u003e \u003cp\u003e2.4.3 Risk Assessment\/Risk Management Challenges of NASA 48\u003c\/p\u003e \u003cp\u003e2.4.4 Review of NASA Risk Assessment\/Risk Management Methodologies 49\u003c\/p\u003e \u003cp\u003e2.5 Nuclear Regulatory Commission (NRC) Overview 49\u003c\/p\u003e \u003cp\u003e2.5.1 NRC Leadership 51\u003c\/p\u003e \u003cp\u003e2.5.2 Critical Steps in NRC Risk Assessment\/Risk Management 52\u003c\/p\u003e \u003cp\u003e2.5.3 Risk Assessment\/Risk Management Challenges of NRC 53\u003c\/p\u003e \u003cp\u003e2.5.4 Review of NRC Risk Assessment\/Risk Management Methodologies 54\u003c\/p\u003e \u003cp\u003e2.6 International Standards Organization (ISO) Overview 55\u003c\/p\u003e \u003cp\u003e2.6.1 ISO Leadership 57\u003c\/p\u003e \u003cp\u003e2.6.2 Critical Steps in ISO Risk Assessment\/Risk Management 57\u003c\/p\u003e \u003cp\u003e2.6.3 Risk Assessment\/Risk Management Challenges of ISO 58\u003c\/p\u003e \u003cp\u003e2.7 Australia Overview 58\u003c\/p\u003e \u003cp\u003e2.7.1 Australia Leadership 59\u003c\/p\u003e \u003cp\u003e2.7.2 Critical Steps in Australia Risk Assessment\/Risk Management 60\u003c\/p\u003e \u003cp\u003e2.7.3 Risk Assessment\/Risk Management Challenges of Australia 61\u003c\/p\u003e \u003cp\u003e2.8 UK Overview 61\u003c\/p\u003e \u003cp\u003e2.8.1 UK Leadership 61\u003c\/p\u003e \u003cp\u003e2.8.2 Critical Steps in UK Risk Assessment\/Risk Management 62\u003c\/p\u003e \u003cp\u003e2.8.3 Risk Assessment\/Risk Management Challenges of the United Kingdom 65\u003c\/p\u003e \u003cp\u003eAcknowledgments 65\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 An Overview of Risk ModelingMethods and Approaches for National Security \u003c\/b\u003e\u003cb\u003e69\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSamrat Chatterjee, Robert T. Brigantic and Angela M.Waterworth\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 69\u003c\/p\u003e \u003cp\u003e3.2 Homeland Security Risk Landscape and Missions 70\u003c\/p\u003e \u003cp\u003e3.2.1 Risk Landscape 71\u003c\/p\u003e \u003cp\u003e3.2.2 Security Missions 71\u003c\/p\u003e \u003cp\u003e3.2.3 Risk Definitions and Interpretations from DHS Risk Lexicon 72\u003c\/p\u003e \u003cp\u003e3.3 Background Review 73\u003c\/p\u003e \u003cp\u003e3.3.1 1960s to 1990s: Focus on Foundational Concepts 73\u003c\/p\u003e \u003cp\u003e3.3.2 The 2000s: Increased Focus on Multi-hazard Risks Including Terrorism 75\u003c\/p\u003e \u003cp\u003e3.3.3 2009 to Present: Emerging Emphasis on System Resilience and Complexity 78\u003c\/p\u003e \u003cp\u003e3.4 Modeling Approaches for Risk Elements 88\u003c\/p\u003e \u003cp\u003e3.4.1 Threat Modeling 88\u003c\/p\u003e \u003cp\u003e3.4.2 VulnerabilityModeling 88\u003c\/p\u003e \u003cp\u003e3.4.2.1 Survey-Based Methods 88\u003c\/p\u003e \u003cp\u003e3.4.2.2 Systems Analysis 89\u003c\/p\u003e \u003cp\u003e3.4.2.3 Network-Theoretic Approaches 89\u003c\/p\u003e \u003cp\u003e3.4.2.4 Structural Analysis and ReliabilityTheory 89\u003c\/p\u003e \u003cp\u003e3.4.3 Consequence Modeling 89\u003c\/p\u003e \u003cp\u003e3.4.3.1 Direct Impacts 89\u003c\/p\u003e \u003cp\u003e3.4.3.2 Indirect Impacts 89\u003c\/p\u003e \u003cp\u003e3.4.4 Risk-Informed Decision Making 90\u003c\/p\u003e \u003cp\u003e3.5 Modeling Perspectives for Further Research 90\u003c\/p\u003e \u003cp\u003e3.5.1 Systemic Risk and ResilienceWithin a Unified Framework 90\u003c\/p\u003e \u003cp\u003e3.5.2 Characterizing Cyber and Physical Infrastructure System Behaviors and Hazards 91\u003c\/p\u003e \u003cp\u003e3.5.3 Utilizing “Big” Data or Lack of Data for Generating Risk and Resilience Analytics 91\u003c\/p\u003e \u003cp\u003e3.5.4 Conceptual Multi-scale, Multi-hazard Modeling Framework 92\u003c\/p\u003e \u003cp\u003e3.6 Concluding Remarks 94\u003c\/p\u003e \u003cp\u003eAcknowledgments 95\u003c\/p\u003e \u003cp\u003eReferences 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Comparative Risk Rankings in Support of Homeland Security Strategic Plans \u003c\/b\u003e\u003cb\u003e101\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRussell Lundberg\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 101\u003c\/p\u003e \u003cp\u003e4.2 Conceptual Challenges in Comparative Risk Ranking 102\u003c\/p\u003e \u003cp\u003e4.3 Practical Challenges in Comparative Ranking of Homeland Security Risks 103\u003c\/p\u003e \u003cp\u003e4.3.1 Choosing a Risk Set 104\u003c\/p\u003e \u003cp\u003e4.3.1.1 Lessons from the DMRR on Hazard Set Selection 105\u003c\/p\u003e \u003cp\u003e4.3.2 Identifying Attributes to Consider 105\u003c\/p\u003e \u003cp\u003e4.3.2.1 Lessons from the DMRR on Attribute Selection 107\u003c\/p\u003e \u003cp\u003e4.3.3 Assessing Each Risk Individually 109\u003c\/p\u003e \u003cp\u003e4.3.3.1 Lessons from the DMRR on Assessing Individual Homeland Security Risks 111\u003c\/p\u003e \u003cp\u003e4.3.4 Combining Individual Risks to Develop a Comparative Risk Ranking 112\u003c\/p\u003e \u003cp\u003e4.3.4.1 Lessons from the DMRR on Comparing Homeland Security Risks 114\u003c\/p\u003e \u003cp\u003e4.4 Policy Relevance to Strategic-Level Homeland Security Risk Rankings 116\u003c\/p\u003e \u003cp\u003e4.4.1 Insights into Homeland Security Risk Rankings 116\u003c\/p\u003e \u003cp\u003e4.4.2 Risk vs. Risk Reduction 118\u003c\/p\u003e \u003cp\u003eAcknowledgments 120\u003c\/p\u003e \u003cp\u003eReferences 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Data ScienceWorkflow for Discovering Spatial Patterns Among Terrorist Attacks and Infrastructure \u003c\/b\u003e\u003cb\u003e125\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDaniel C. Fortin, Thomas Johansen, Samrat Chatterjee, GeorgeMuller and Christine Noonan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 125\u003c\/p\u003e \u003cp\u003e5.2 The Data: Global Terrorism Database 126\u003c\/p\u003e \u003cp\u003e5.3 The Tools: Exploring Data Interactively Using a Custom Shiny App 127\u003c\/p\u003e \u003cp\u003e5.4 Example: Using the App to Explore ISIL Attacks 130\u003c\/p\u003e \u003cp\u003e5.5 TheModels: StatisticalModels for Terrorist Event Data 134\u003c\/p\u003e \u003cp\u003e5.6 More Data: Obtaining Regional Infrastructure Data to Build Statistical Models 135\u003c\/p\u003e \u003cp\u003e5.7 A Model: Determining the Significance of Infrastructure on the Likelihood of an Attack 137\u003c\/p\u003e \u003cp\u003e5.8 Case Study: Libya 138\u003c\/p\u003e \u003cp\u003e5.9 Case Study: Jammu and Kashmir Region of India 139\u003c\/p\u003e \u003cp\u003e5.9.1 The Model Revisited: Accounting for Many Regions with No Recorded Attacks 141\u003c\/p\u003e \u003cp\u003e5.9.2 Investigating the Effect of Outliers 145\u003c\/p\u003e \u003cp\u003e5.9.3 The Insight: What Have We Learned? 147\u003c\/p\u003e \u003cp\u003e5.10 Summary 148\u003c\/p\u003e \u003cp\u003eReferences 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Strengthening Ports of Entry \u003c\/b\u003e\u003cb\u003e151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Effects of Credibility of Retaliation Threats in Deterring Smuggling of Nuclear Weapons \u003c\/b\u003e\u003cb\u003e153\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eXiaojun Shan and Jun Zhuang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 153\u003c\/p\u003e \u003cp\u003e6.2 Extending Prior Game-Based Model 158\u003c\/p\u003e \u003cp\u003e6.3 Comparing the Game Trees 158\u003c\/p\u003e \u003cp\u003e6.4 The Extended Model 161\u003c\/p\u003e \u003cp\u003e6.5 Solution to the Extended Model 162\u003c\/p\u003e \u003cp\u003e6.6 Comparing the Solutions in Prior Game-Based Model and This Study 163\u003c\/p\u003e \u003cp\u003e6.7 Illustration of the Extended Model Using Real Data 164\u003c\/p\u003e \u003cp\u003e6.8 Conclusion and Future Research Work 165\u003c\/p\u003e \u003cp\u003eReferences 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Disutility of Mass Relocation After a Severe Nuclear Accident \u003c\/b\u003e\u003cb\u003e171\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVickiM. Bier and Shuji Liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 171\u003c\/p\u003e \u003cp\u003e7.2 Raw Data 174\u003c\/p\u003e \u003cp\u003e7.3 Trade-Offs Between Cancer Fatalities and Relocation 177\u003c\/p\u003e \u003cp\u003e7.4 Risk-Neutral DisutilityModel 179\u003c\/p\u003e \u003cp\u003e7.5 Risk-Averse DisutilityModel 179\u003c\/p\u003e \u003cp\u003e7.6 DisutilityModel with Interaction Effects 182\u003c\/p\u003e \u003cp\u003e7.7 Economic Analysis 185\u003c\/p\u003e \u003cp\u003e7.8 Conclusion 190\u003c\/p\u003e \u003cp\u003eReferences 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Scheduling Federal Air Marshals Under Uncertainty \u003c\/b\u003e\u003cb\u003e193\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKeithW. DeGregory and Rajesh Ganesan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 193\u003c\/p\u003e \u003cp\u003e8.2 Literature 196\u003c\/p\u003e \u003cp\u003e8.2.1 Commercial Aviation Industry 196\u003c\/p\u003e \u003cp\u003e8.2.2 Homeland Security and the Federal Air Marshals Service 198\u003c\/p\u003e \u003cp\u003e8.2.3 Approximate Dynamic Programming 199\u003c\/p\u003e \u003cp\u003e8.3 Air Marshal Resource Allocation Model 200\u003c\/p\u003e \u003cp\u003e8.3.1 Risk Model 200\u003c\/p\u003e \u003cp\u003e8.3.2 Static Allocation 202\u003c\/p\u003e \u003cp\u003e8.3.3 Dynamic Allocation 203\u003c\/p\u003e \u003cp\u003e8.4 Stochastic Dynamic Programming Formulation 204\u003c\/p\u003e \u003cp\u003e8.4.1 System State 205\u003c\/p\u003e \u003cp\u003e8.4.2 Decision Variable 205\u003c\/p\u003e \u003cp\u003e8.4.3 Post-decision State 206\u003c\/p\u003e \u003cp\u003e8.4.4 Exogenous Information 206\u003c\/p\u003e \u003cp\u003e8.4.5 State Transition Function 206\u003c\/p\u003e \u003cp\u003e8.4.6 Contribution Function 206\u003c\/p\u003e \u003cp\u003e8.4.7 Objective Function 207\u003c\/p\u003e \u003cp\u003e8.4.8 Bellman’s Optimality Equations 207\u003c\/p\u003e \u003cp\u003e8.5 Phases of Stochastic Dynamic Programming 207\u003c\/p\u003e \u003cp\u003e8.5.1 Exploration Phase 207\u003c\/p\u003e \u003cp\u003e8.5.2 Learning Phase 208\u003c\/p\u003e \u003cp\u003e8.5.2.1 Algorithm 208\u003c\/p\u003e \u003cp\u003e8.5.2.2 Approximation Methods 208\u003c\/p\u003e \u003cp\u003e8.5.2.3 Convergence 209\u003c\/p\u003e \u003cp\u003e8.5.3 Learned Phase 210\u003c\/p\u003e \u003cp\u003e8.6 Integrated Allocation Model 210\u003c\/p\u003e \u003cp\u003e8.7 Results 211\u003c\/p\u003e \u003cp\u003e8.7.1 Experiment 211\u003c\/p\u003e \u003cp\u003e8.7.2 Results from Stochastic Dynamic Programming Model 211\u003c\/p\u003e \u003cp\u003e8.7.3 Sensitivity Analysis 212\u003c\/p\u003e \u003cp\u003e8.7.4 Model Output 214\u003c\/p\u003e \u003cp\u003e8.8 Conclusion 217\u003c\/p\u003e \u003cp\u003eAcknowledgments 218\u003c\/p\u003e \u003cp\u003eReferences 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Securing Critical Cyber Assets \u003c\/b\u003e\u003cb\u003e221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Decision Theory for Network Security: Active Sensing for Detection and Prevention of Data Exfiltration \u003c\/b\u003e\u003cb\u003e223\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSara M. McCarthy, Arunesh Sinha,Milind Tambe and Pratyusa Manadhatha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 223\u003c\/p\u003e \u003cp\u003e9.1.1 Problem Domain 224\u003c\/p\u003e \u003cp\u003e9.2 Background and RelatedWork 226\u003c\/p\u003e \u003cp\u003e9.2.1 DNS Exfiltration 226\u003c\/p\u003e \u003cp\u003e9.2.2 Partially Observable Markov Decision Process (POMDP) 228\u003c\/p\u003e \u003cp\u003e9.3 Threat Model 229\u003c\/p\u003e \u003cp\u003e9.3.1 The POMDP Model 230\u003c\/p\u003e \u003cp\u003e9.4 POMDP Abstraction 232\u003c\/p\u003e \u003cp\u003e9.4.1 Abstract Actions 232\u003c\/p\u003e \u003cp\u003e9.4.2 Abstract Observations 234\u003c\/p\u003e \u003cp\u003e9.4.3 VD-POMDP Factored Representation 234\u003c\/p\u003e \u003cp\u003e9.4.4 Policy Execution 236\u003c\/p\u003e \u003cp\u003e9.5 VD-POMDP Framework 239\u003c\/p\u003e \u003cp\u003e9.6 Evaluation 241\u003c\/p\u003e \u003cp\u003e9.6.1 Synthetic Networks 241\u003c\/p\u003e \u003cp\u003e9.6.2 DETER Testbed Simulation 241\u003c\/p\u003e \u003cp\u003e9.6.3 Runtime 242\u003c\/p\u003e \u003cp\u003e9.6.4 Performance 244\u003c\/p\u003e \u003cp\u003e9.6.5 Robustness 246\u003c\/p\u003e \u003cp\u003e9.7 GameTheoretic Extensions 247\u003c\/p\u003e \u003cp\u003e9.7.1 Threat Model 248\u003c\/p\u003e \u003cp\u003e9.8 Conclusion and FutureWork 249\u003c\/p\u003e \u003cp\u003eAcknowledgments 249\u003c\/p\u003e \u003cp\u003eReferences 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Measurement of Cyber Resilience from an Economic Perspective \u003c\/b\u003e\u003cb\u003e253\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAdam Z. Rose and NoahMiller\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 253\u003c\/p\u003e \u003cp\u003e10.2 Economic Resilience 254\u003c\/p\u003e \u003cp\u003e10.2.1 Basic Concepts of Cyber Resilience 254\u003c\/p\u003e \u003cp\u003e10.2.2 Basic Concepts of Economic Resilience 254\u003c\/p\u003e \u003cp\u003e10.2.3 Economic Resilience Metrics 255\u003c\/p\u003e \u003cp\u003e10.3 Cyber System Resilience Tactics 257\u003c\/p\u003e \u003cp\u003e10.4 Resilience for Cyber-Related Sectors 267\u003c\/p\u003e \u003cp\u003e10.4.1 Resilience in the Manufacturing of Cyber Equipment 267\u003c\/p\u003e \u003cp\u003e10.4.2 Resilience in the Electricity Sector 268\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 269\u003c\/p\u003e \u003cp\u003eReferences 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Responses to Cyber Near-Misses: A Scale to Measure Individual Differences \u003c\/b\u003e\u003cb\u003e275\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJinshu Cui, Heather Rosoff and Richard S. John\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 275\u003c\/p\u003e \u003cp\u003e11.2 Scale Development and Analysis Outline 277\u003c\/p\u003e \u003cp\u003e11.3 Method 278\u003c\/p\u003e \u003cp\u003e11.3.1 Measures 278\u003c\/p\u003e \u003cp\u003e11.3.1.1 Cyber Near-Miss Appraisal Scale (CNMAS) 278\u003c\/p\u003e \u003cp\u003e11.3.1.2 Measures of Discriminant Validity 281\u003c\/p\u003e \u003cp\u003e11.3.1.3 Measure of Predictive Validity 281\u003c\/p\u003e \u003cp\u003e11.3.1.4 Participants and Procedures 281\u003c\/p\u003e \u003cp\u003e11.4 Results 284\u003c\/p\u003e \u003cp\u003e11.4.1 Dimensionality and Reliability 284\u003c\/p\u003e \u003cp\u003e11.4.2 Item Response Analysis 284\u003c\/p\u003e \u003cp\u003e11.4.3 Differential Item Functioning (DIF) 287\u003c\/p\u003e \u003cp\u003e11.4.4 Effects of Demographic Variables 289\u003c\/p\u003e \u003cp\u003e11.4.5 Discriminant Validity 290\u003c\/p\u003e \u003cp\u003e11.4.6 Predictive Validity 290\u003c\/p\u003e \u003cp\u003e11.5 Discussion 291\u003c\/p\u003e \u003cp\u003eAcknowledgments 292\u003c\/p\u003e \u003cp\u003eReferences 292\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Enhancing Disaster Preparedness and Infrastructure Resilience \u003c\/b\u003e\u003cb\u003e295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 An InteractiveWeb-Based Decision Support Systemfor Mass Dispensing, Emergency Preparedness, and Biosurveillance \u003c\/b\u003e\u003cb\u003e297\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEva K. Lee, Ferdinand H. Pietz, Chien-Hung Chen and Yifan Liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 297\u003c\/p\u003e \u003cp\u003e12.2 System Architecture and Design 299\u003c\/p\u003e \u003cp\u003e12.3 System Modules and Functionalities 301\u003c\/p\u003e \u003cp\u003e12.3.1 Interactive User Experience 301\u003c\/p\u003e \u003cp\u003e12.3.2 Geographical Boundaries 301\u003c\/p\u003e \u003cp\u003e12.3.3 Network of Service, Locations, and Population Flow and Assignment 302\u003c\/p\u003e \u003cp\u003e12.3.4 ZIP Code and Population Composition 304\u003c\/p\u003e \u003cp\u003e12.3.5 Multimodality Dispensing and Public–Private Partnership 305\u003c\/p\u003e \u003cp\u003e12.3.6 POD Layout Design and Resource Allocation 308\u003c\/p\u003e \u003cp\u003e12.3.7 Radiological Module 309\u003c\/p\u003e \u003cp\u003e12.3.8 Biosurveillance 309\u003c\/p\u003e \u003cp\u003e12.3.9 Regional Information Sharing, Reverse Reporting, Tracking and Monitoring, and Resupply 310\u003c\/p\u003e \u003cp\u003e12.3.10 Multilevel End-User Access 311\u003c\/p\u003e \u003cp\u003e12.4 Biodefense, Pandemic Preparedness Planning, and Radiological and Large-Scale Disaster Relief Efforts 312\u003c\/p\u003e \u003cp\u003e12.4.1 Biodefense Mass Dispensing Regional Planning 312\u003c\/p\u003e \u003cp\u003e12.4.2 Real-Life Disaster Response Effort 315\u003c\/p\u003e \u003cp\u003e12.4.2.1 RealOpt-Haiti© 315\u003c\/p\u003e \u003cp\u003e12.4.2.2 RealOpt-Regional and RealOpt-CRC for Fukushima Daiichi Nuclear Disaster 316\u003c\/p\u003e \u003cp\u003e12.4.2.3 RealOpt-ASSURE© 318\u003c\/p\u003e \u003cp\u003e12.5 Challenges and Conclusions 319\u003c\/p\u003e \u003cp\u003eAcknowledgments 321\u003c\/p\u003e \u003cp\u003eReferences 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Measuring Critical Infrastructure Risk, Protection, and Resilience in an All-Hazards Environment \u003c\/b\u003e\u003cb\u003e325\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJulia A. Phillips and Frédéric Petit\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction to Critical Infrastructure Risk Assessment 325\u003c\/p\u003e \u003cp\u003e13.2 Motivation for Critical Infrastructure Risk Assessments 326\u003c\/p\u003e \u003cp\u003e13.2.1 Unrest pre-September 2001 326\u003c\/p\u003e \u003cp\u003e13.2.2 Post-911 Critical Infrastructure Protection and Resilience 326\u003c\/p\u003e \u003cp\u003e13.3 Decision Analysis Methodologies for Creating Critical Infrastructure Risk Indicators 327\u003c\/p\u003e \u003cp\u003e13.3.1 Decision Analysis 328\u003c\/p\u003e \u003cp\u003e13.3.2 Illustrative Calculations for an Index: Buying a Car 328\u003c\/p\u003e \u003cp\u003e13.4 An Application of Critical Infrastructure Protection, Consequence, and Resilience Assessment 331\u003c\/p\u003e \u003cp\u003e13.4.1 Protection and Vulnerability 334\u003c\/p\u003e \u003cp\u003e13.4.1.1 Physical Security 335\u003c\/p\u003e \u003cp\u003e13.4.1.2 Security Management 335\u003c\/p\u003e \u003cp\u003e13.4.1.3 Security Force 335\u003c\/p\u003e \u003cp\u003e13.4.1.4 Information Sharing 337\u003c\/p\u003e \u003cp\u003e13.4.1.5 Security Activity Background 338\u003c\/p\u003e \u003cp\u003e13.4.2 Resilience 339\u003c\/p\u003e \u003cp\u003e13.4.2.1 Preparedness 341\u003c\/p\u003e \u003cp\u003e13.4.2.2 Mitigation Measures 341\u003c\/p\u003e \u003cp\u003e13.4.2.3 Response Capabilities 342\u003c\/p\u003e \u003cp\u003e13.4.2.4 Recovery Mechanisms 343\u003c\/p\u003e \u003cp\u003e13.4.3 Consequences 343\u003c\/p\u003e \u003cp\u003e13.4.3.1 Human Consequences 345\u003c\/p\u003e \u003cp\u003e13.4.3.2 Economic Consequences 346\u003c\/p\u003e \u003cp\u003e13.4.3.3 Government Mission\/Public Health\/Psychological Consequences 346\u003c\/p\u003e \u003cp\u003e13.4.3.4 Cascading Impact Consequences 347\u003c\/p\u003e \u003cp\u003e13.4.4 Risk Indices Comparison 349\u003c\/p\u003e \u003cp\u003e13.5 Infrastructure Interdependencies 350\u003c\/p\u003e \u003cp\u003e13.6 What’s Next for Critical Infrastructure Risk Assessments 352\u003c\/p\u003e \u003cp\u003eReferences 354\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Risk AnalysisMethods in Resilience Modeling: An Overview of Critical Infrastructure Applications \u003c\/b\u003e\u003cb\u003e357\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHiba Baroud\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 357\u003c\/p\u003e \u003cp\u003e14.2 Background 358\u003c\/p\u003e \u003cp\u003e14.2.1 Risk Analysis 358\u003c\/p\u003e \u003cp\u003e14.2.2 Resilience 359\u003c\/p\u003e \u003cp\u003e14.2.3 Critical Infrastructure Systems 360\u003c\/p\u003e \u003cp\u003e14.3 Modeling the Resilience of Critical Infrastructure Systems 361\u003c\/p\u003e \u003cp\u003e14.3.1 Resilience Models 361\u003c\/p\u003e \u003cp\u003e14.3.1.1 Manufacturing 361\u003c\/p\u003e \u003cp\u003e14.3.1.2 Communications 362\u003c\/p\u003e \u003cp\u003e14.3.1.3 Dams, Levees, andWaterways 363\u003c\/p\u003e \u003cp\u003e14.3.1.4 Defense 363\u003c\/p\u003e \u003cp\u003e14.3.1.5 Emergency Services 363\u003c\/p\u003e \u003cp\u003e14.3.1.6 Energy 363\u003c\/p\u003e \u003cp\u003e14.3.1.7 Transportation 364\u003c\/p\u003e \u003cp\u003e14.3.1.8 Water\/Wastewater 364\u003c\/p\u003e \u003cp\u003e14.3.2 Discussion 365\u003c\/p\u003e \u003cp\u003e14.3.2.1 Economic Impact 365\u003c\/p\u003e \u003cp\u003e14.3.2.2 Social Impact 367\u003c\/p\u003e \u003cp\u003e14.3.2.3 Interdependencies 367\u003c\/p\u003e \u003cp\u003e14.4 Assessing Risk in Resilience Models 368\u003c\/p\u003e \u003cp\u003e14.4.1 Probabilistic Methods 368\u003c\/p\u003e \u003cp\u003e14.4.2 UncertaintyModeling 369\u003c\/p\u003e \u003cp\u003e14.4.3 Simulation-Based Approaches 369\u003c\/p\u003e \u003cp\u003e14.4.4 Data-Driven Analytics 370\u003c\/p\u003e \u003cp\u003e14.5 Opportunities and Challenges 370\u003c\/p\u003e \u003cp\u003e14.5.1 Opportunities 370\u003c\/p\u003e \u003cp\u003e14.5.2 Challenges 371\u003c\/p\u003e \u003cp\u003e14.6 Concluding Remarks 372\u003c\/p\u003e \u003cp\u003eReferences 373\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Optimal Resource Allocation Model to Prevent, Prepare, and Respond to Multiple Disruptions, with Application to the \u003ci\u003eDeepwater Horizon \u003c\/i\u003eOil Spill and Hurricane Katrina \u003c\/b\u003e\u003cb\u003e381\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eCameron A.MacKenzie and Amro Al Kazimi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 381\u003c\/p\u003e \u003cp\u003e15.2 Model Development 383\u003c\/p\u003e \u003cp\u003e15.2.1 Resource Allocation Model 383\u003c\/p\u003e \u003cp\u003e15.2.2 Extension to Uncertain Parameters 385\u003c\/p\u003e \u003cp\u003e15.3 Application: \u003ci\u003eDeepwater Horizon \u003c\/i\u003eand Hurricane Katrina 386\u003c\/p\u003e \u003cp\u003e15.3.1 Parameter Estimation 386\u003c\/p\u003e \u003cp\u003e15.3.1.1 Oil Spill Parameters 387\u003c\/p\u003e \u003cp\u003e15.3.1.2 Hurricane Parameters 388\u003c\/p\u003e \u003cp\u003e15.3.2 Base Case Results 391\u003c\/p\u003e \u003cp\u003e15.3.3 Sensitivity Analysis on Economic Impacts 394\u003c\/p\u003e \u003cp\u003e15.3.4 Model with Uncertain Effectiveness 395\u003c\/p\u003e \u003cp\u003e15.4 Conclusions 397\u003c\/p\u003e \u003cp\u003eReferences 398\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Inoperability Input–Output Modeling of Electric Power Disruptions \u003c\/b\u003e\u003cb\u003e405\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJoost R. Santos, Sheree Ann Pagsuyoin and Christian Yip\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 405\u003c\/p\u003e \u003cp\u003e16.2 Risk Analysis of Natural and Man-Caused Electric Power Disruptions 407\u003c\/p\u003e \u003cp\u003e16.3 Risk Management Insights for Disruptive Events 408\u003c\/p\u003e \u003cp\u003e16.4 Modeling the Ripple Effects for Disruptive Events 411\u003c\/p\u003e \u003cp\u003e16.5 Inoperability Input–Output Model 412\u003c\/p\u003e \u003cp\u003e16.5.1 Model Parameters 412\u003c\/p\u003e \u003cp\u003e16.5.2 Sector Inoperability 413\u003c\/p\u003e \u003cp\u003e16.5.3 InterdependencyMatrix 413\u003c\/p\u003e \u003cp\u003e16.5.4 Demand Perturbation 414\u003c\/p\u003e \u003cp\u003e16.5.5 Economic Resilience 414\u003c\/p\u003e \u003cp\u003e16.5.6 Economic Loss 415\u003c\/p\u003e \u003cp\u003e16.6 Sample Electric Power Disruptions Scenario Analysis for the United States 416\u003c\/p\u003e \u003cp\u003e16.7 Summary and Conclusions 421\u003c\/p\u003e \u003cp\u003eReferences 422\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Quantitative Assessment of Transportation Network Vulnerability with Dynamic Traffic Simulation Methods \u003c\/b\u003e\u003cb\u003e427\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVenkateswaran Shekar and Lance Fiondella\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 427\u003c\/p\u003e \u003cp\u003e17.2 Dynamic Transportation Network Vulnerability Assessment 429\u003c\/p\u003e \u003cp\u003e17.3 Sources of Input for Dynamic Transportation Network Vulnerability Assessment 431\u003c\/p\u003e \u003cp\u003e17.4 Illustrations 432\u003c\/p\u003e \u003cp\u003e17.4.1 Example 1: Simple Network 432\u003c\/p\u003e \u003cp\u003e17.4.2 Example II: University of Massachusetts Dartmouth Evacuation 437\u003c\/p\u003e \u003cp\u003e17.5 Conclusion and Future Research 439\u003c\/p\u003e \u003cp\u003eReferences 440\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Infrastructure Monitoring for Health and Security \u003c\/b\u003e\u003cb\u003e443\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eProdyot K. Basu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 443\u003c\/p\u003e \u003cp\u003e18.2 Data Acquisition 447\u003c\/p\u003e \u003cp\u003e18.3 Sensors 447\u003c\/p\u003e \u003cp\u003e18.3.1 Underlying Principles of Some of the Popular Sensors Listed in Table 18.1 451\u003c\/p\u003e \u003cp\u003e18.3.1.1 Fiber Optics 451\u003c\/p\u003e \u003cp\u003e18.3.1.2 VibratingWire 451\u003c\/p\u003e \u003cp\u003e18.3.1.3 Piezoelectric Sensors 456\u003c\/p\u003e \u003cp\u003e18.3.1.4 Piezoresistive Sensors 456\u003c\/p\u003e \u003cp\u003e18.3.1.5 Laser Vibrometer 456\u003c\/p\u003e \u003cp\u003e18.3.1.6 Acoustic Emission Sensing 457\u003c\/p\u003e \u003cp\u003e18.3.1.7 GPS and GNSS 458\u003c\/p\u003e \u003cp\u003e18.3.2 Selection of a Sensor 459\u003c\/p\u003e \u003cp\u003e18.4 Capturing and Transmitting Signals 459\u003c\/p\u003e \u003cp\u003e18.5 Energy Harvesting 461\u003c\/p\u003e \u003cp\u003e18.6 Robotic IHM 462\u003c\/p\u003e \u003cp\u003e18.7 Cyber-Physical Systems 464\u003c\/p\u003e \u003cp\u003e18.8 Conclusions 464\u003c\/p\u003e \u003cp\u003eReferences 465\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Exploring Metaheuristic Approaches for Solving the Traveling Salesman Problem Applied to Emergency Planning and Response \u003c\/b\u003e\u003cb\u003e467\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRamakrishna Tipireddy, Javier Rubio-Herrero, Samrat Chatterjee and Satish Chikkagoudar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 The Traveling Salesman Problem 467\u003c\/p\u003e \u003cp\u003e19.1.1 Definition 467\u003c\/p\u003e \u003cp\u003e19.1.2 Computational Complexity 467\u003c\/p\u003e \u003cp\u003e19.1.3 Solution Algorithms 468\u003c\/p\u003e \u003cp\u003e19.1.4 Emergency Response Application 468\u003c\/p\u003e \u003cp\u003e19.2 Emergency Planning and Response as a Traveling Salesman Problem 468\u003c\/p\u003e \u003cp\u003e19.3 Metaheuristic Approaches 469\u003c\/p\u003e \u003cp\u003e19.3.1 Simulated Annealing 470\u003c\/p\u003e \u003cp\u003e19.3.1.1 Overview 470\u003c\/p\u003e \u003cp\u003e19.3.1.2 Pseudocode 471\u003c\/p\u003e \u003cp\u003e19.3.1.3 Case Study Results 473\u003c\/p\u003e \u003cp\u003e19.3.2 Tabu Search 473\u003c\/p\u003e \u003cp\u003e19.3.2.1 Overview 473\u003c\/p\u003e \u003cp\u003e19.3.2.2 Pseudocode 474\u003c\/p\u003e \u003cp\u003e19.3.2.3 Case Study Results 476\u003c\/p\u003e \u003cp\u003e19.3.3 Genetic Algorithms 476\u003c\/p\u003e \u003cp\u003e19.3.3.1 Overview 476\u003c\/p\u003e \u003cp\u003e19.3.3.2 Pseudocode 478\u003c\/p\u003e \u003cp\u003e19.3.3.3 Case Study Results 479\u003c\/p\u003e \u003cp\u003e19.3.4 Ant Colony Optimization 479\u003c\/p\u003e \u003cp\u003e19.3.4.1 Overview 479\u003c\/p\u003e \u003cp\u003e19.3.4.2 Stochastic Solution Construction 480\u003c\/p\u003e \u003cp\u003e19.3.4.3 Pheromone Update 480\u003c\/p\u003e \u003cp\u003e19.3.4.4 Pseudocode 481\u003c\/p\u003e \u003cp\u003e19.3.4.5 Case Study Results 481\u003c\/p\u003e \u003cp\u003e19.4 Discussion 482\u003c\/p\u003e \u003cp\u003e19.5 Concluding Remarks 482\u003c\/p\u003e \u003cp\u003eReferences 484\u003c\/p\u003e \u003cp\u003eIndex 487\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSAMRAT CHATTERJEE, PhD,\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eROBERT T. BRIGANTIC, PhD,\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eANGELA M. WATERWORTH, MS,\u003c\/b\u003e is Senior Operations Research\/Data Scientist in the Computing and Analytics Division within the National Security Directorate at PNNL.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePresents various challenges faced by security policy makers and risk analysts, and mathematical approaches that inform homeland security policy development and decision support\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCompiled 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. \u003c\/p\u003e \u003cp\u003e\u003ci\u003eApplied Risk Analysis for Guiding Homeland Security Policy and Decisions\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eHighlights how risk analysis helps in the decision-making process for homeland security policy\u003c\/li\u003e \u003cli\u003ePresents specific examples that detail how various risk analysis methods provide decision support for homeland security policy makers and risk analysts\u003c\/li\u003e \u003cli\u003eDescribes 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)\u003c\/li\u003e \u003cli\u003eOffers 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\u003c\/li\u003e \u003cli\u003eDiscusses 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\u003c\/li\u003e \u003cli\u003eWritten by top educators and professionals who clearly illustrate the link between risk science and homeland security policy making \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Risk Analysis for Guiding Homeland Security Policy and Decisions\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988753629413,"sku":"NP9781119287469","price":135.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119287469.jpg?v=1761781456","url":"https:\/\/k12savings.com\/es\/products\/applied-risk-analysis-for-guiding-homeland-security-policy-and-decisions-isbn-9781119287469","provider":"K12savings","version":"1.0","type":"link"}