Literature DB >> 18076496

A framework for linking cybersecurity metrics to the modeling of macroeconomic interdependencies.

Joost R Santos1, Yacov Y Haimes, Chenyang Lian.   

Abstract

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 (I-O) 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.

Year:  2007        PMID: 18076496     DOI: 10.1111/j.1539-6924.2007.00957.x

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  3 in total

1.  Risk-based input-output analysis of influenza epidemic consequences on interdependent workforce sectors.

Authors:  Joost R Santos; Larissa May; Amine El Haimar
Journal:  Risk Anal       Date:  2012-12-24       Impact factor: 4.000

2.  Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber-Physical Attacks on Electricity Distribution Infrastructure Networks.

Authors:  Edward J Oughton; Daniel Ralph; Raghav Pant; Eireann Leverett; Jennifer Copic; Scott Thacker; Rabia Dada; Simon Ruffle; Michelle Tuveson; Jim W Hall
Journal:  Risk Anal       Date:  2019-02-27       Impact factor: 4.000

3.  A Risk Assessment Framework Proposal Based on Bow-Tie Analysis for Medical Image Diagnosis Sharing within Telemedicine.

Authors:  Thiago Poleto; Maisa Mendonça Silva; Thárcylla Rebecca Negreiros Clemente; Ana Paula Henriques de Gusmão; Ana Paula de Barros Araújo; Ana Paula Cabral Seixas Costa
Journal:  Sensors (Basel)       Date:  2021-04-01       Impact factor: 3.576

  3 in total

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