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==Full Title of Reference== A Framework for Linking Cybersecurity Metrics to the Modeling of Macroeconomic Interdependencies ==Full Citation== Joost R. Santos, Yacov Y. Haimes and Chenyang Lian, ''A Framework for Linking Cybersecurity Metrics to the Modeling of Macroeconomic Interdependencies'', 27 Risk Analysis 5 (2007). [http://www3.interscience.wiley.com/journal/118486550/abstract?CRETRY=1&SRETRY=0 ''Purchase''] [http://cyber.law.harvard.edu/cybersecurity/?title=Special:Bibliography&view=detailed&startkey=Santos_et_al:2007&f=wikibiblio.bib ''BibTeX''] ==Categorization== Threats and Actors: [[Private Critical Infrastructure]] Issues: [[Metrics]] ==Key Words== [http://cyber.law.harvard.edu/cybersecurity/Glossary_of_Core_Ideas#Interdependencies Interdependencies], [http://cyber.law.harvard.edu/cybersecurity/Glossary_of_Core_Ideas#Risk_Modeling Risk Modeling] ==Synopsis== Hierarchical decision making is a multidimensional process involving management of multiple objectives (with associated metrics and tradeoffs in terms of costs, benefits, and risks), which span various levels of a large-scale system. The nation is a hierarchical system as it consists multiple classes of decisionmakers and stakeholders ranging from national policymakers to operators of specific critical infrastructure subsystems. Critical infrastructures (e.g., transportation, telecommunications, power, banking, etc.) are highly complex and interconnected. These interconnections take the form of flows of information, shared security, and physical flows of commodities, among others. In recent years, economic and infrastructure sectors have become increasingly dependent on networked information systems for efficient operations and timely delivery of products and services. In order to ensure the stability, sustainability, and operability of our critical economic and infrastructure sectors, it is imperative to understand their inherent physical and economic linkages, in addition to their cyber interdependencies. An interdependency model based on a transformation of the Leontief input-output (1-0) model can be used for modeling: (1) the steady-state economic effects triggered by a consumption shift in a given sector (or set of sectors); and (2) the resulting ripple effects to other sectors. The inoperability metric is calculated for each sector; this is achieved by converting the economic impact (typically in monetary units) into a percentage value relative to the size of the sector. Disruptive events such as terrorist attacks, natural disasters, and large-scale accidents have historically shown cascading effects on both consumption and production. Hence, a dynamic model extension is necessary to demonstrate the interplay between combined demand and supply effects. The result is a foundational framework for modeling cybersecurity scenarios for the oil and gas sector. A hypothetical case study examines a cyber attack that causes a 5-week shortfall in the crude oil supply in the Gulf Coast area. ==Additional Notes and Highlights== The case study regarding a cyber attack in the oil and gas industry is particularly interesting.

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