Defining resilience analytics for interdependent cyber-physical-social networks

Barker, Kash; Lambert, James H.; Zobel, Christopher W.; Tapia, Andrea H.; Ramírez-Márquez, José Emmanuel; Albert, Laura A.; Nicholson, Charles; Caragea, Cornelia

doi:10.1080/23789689.2017.1294859

Cited by 70 publications

(40 citation statements)

References 30 publications

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“…This requires federated learning and blockchain based decentralised AI architecture where AI processing shifts from the cloud to the edge and the AI workflow is moved and data restricted to the device [37]. Current gaps in cyber risk analytics are in the areas of descriptive, predictive, and prescriptive analytics [38]. Hence, a survey of AI/ML applications is presented in Error!…”

Section: Survey Of Ai/ml Algorithmsmentioning

confidence: 99%

Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON)

Radanliev¹

2020

Preprint

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This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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Section: Survey Of Ai/ml Algorithmsmentioning

confidence: 99%

Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON)

Radanliev¹

2020

Preprint

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“…By obtaining quality data according to the proposed data aggregation model, RM and analysis can be conducted to support decision-making. [63] defines three types of resilience analytics, namely descriptive, predictive, and prescriptive analytics. Descriptive analytics depicts the statistical characteristics of the performance of interdependent infrastructure systems before, during, and after disruptions; while predictive analytics focuses on quantifying the likelihood of future adverse events and their effects; while prescriptive analytics aims to identify a feasible course of interventions and strategies to best achieve the systems' resilient objectives.…”

Section: Performance Metricsmentioning

confidence: 99%

Towards Resilient Civil Infrastructure Asset Management: An Information Elicitation and Analytical Framework

Yang

et al. 2019

Sustainability

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It is rather difficult for the stakeholders to understand and implement the resilience concept and principles in the infrastructure asset management paradigm, as it demands quality data, holistic information integration and competent data analytics capabilities to identify infrastructure vulnerabilities, evaluate and predict infrastructure adaptabilities to different hazards, as well as to make damage restoration and resilience improvement strategies and plans. To meet the stakeholder’s urgent needs, this paper proposes an information elicitation and analytical framework for resilient infrastructure asset management. The framework is devised by leveraging the best practices and processes of integrated infrastructure asset management and resilience management in the literature, synergizing the common elements and critical concepts of the two paradigms, ingesting the state-of-the-art interconnected infrastructure systems resilience analytical approaches, and eliciting expert judgments to iteratively improve the derived framework. To facilitate the stakeholders in implementing the framework, two use case studies are given in this paper, depicting the detailed workflow for information integration and resilience analytics in infrastructure asset management. The derived framework is expected to provide an operational basis to the quantitative resilience management of civil infrastructure assets, which could also be used to enhance community resilience.

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“…Energy mobility networks are interdependent and interconnected networks that include social networks, cyber networks, and physical networks. Recent studies have highlighted the importance of investigating the interdependency and interconnectedness of these networks from different perspectives (Andrijcic & Haimes, ; Barker et al., ). Energy mobility has been defined in this article as the ability to use plug‐in electric vehicles (PEVs) as energy storage for balancing grid services.…”

Section: Introductionmentioning

confidence: 99%

Integrating Stakeholder Mapping and Risk Scenarios to Improve Resilience of Cyber‐Physical‐Social Networks

Almutairi

Wheeler²,

Slutzky

et al. 2019

Risk Analysis

Self Cite

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The future of energy mobility involves networks of users, operators, organizations, vehicles, charging stations, communications, materials, transportation corridors, points of service, and so on. The integration of smart grids with plug-in electric vehicle technologies has societal and commercial advantages that include improving grid stability, minimizing dependence on nonrenewable fuels, reducing vehicle emissions, and reducing the cost of electric vehicle ownership. However, ineffective or delayed participation of particular groups of stakeholders could disrupt industry plans and delay the desired outcomes. This article develops a framework to address enterprise resilience for two modes of disruptions-the first being the influence of scenarios on priorities and the second being the influence of multiple groups of stakeholders on priorities. The innovation of this study is to obtain the advantages of integrating two recent approaches: scenario-based preferences modeling and stakeholder mapping. Public agencies, grid operators, plug-in electric vehicle owners, and vehicle manufacturers are the four groups of stakeholders that are considered in this framework, along with the influence of four scenarios on priorities.

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Defining resilience analytics for interdependent cyber-physical-social networks

Cited by 70 publications

References 30 publications

Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON)

Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) -forecasting: Red Teaming the Connected World (RETCON)

Towards Resilient Civil Infrastructure Asset Management: An Information Elicitation and Analytical Framework

Integrating Stakeholder Mapping and Risk Scenarios to Improve Resilience of Cyber‐Physical‐Social Networks

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