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Methodological Resilience Assessment of Smart Cyber Infrastructures

  • Institut de Recherche Technologique SystemX
  • Carleton University, School of Computer Science

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Chapitre › Revue par des pairs

1 Citation (Scopus)

Résumé

The race for digitization created a real need to protect smart infrastructures. Environments are becoming highly connected and automated. Their growing complexity and connectivity make it hard to assure and assess their cyber resilience, i.e., protecting them from cyberattacks, failures, and errors. Traditional strategies for ensuring the cyber resilience of smart infrastructures suffer from a lack of holism. Indeed, since smart infrastructures are often structured in layers, traditional protection methods can lead to conflicting and competing goals. For instance, they may increase the resilience of specific layers at the expense of decreasing the performance of others. This chapter reviews existing methods aiming to address this problem. We focus on two leading methodological assessment families: quantitative and qualitative. The former includes numerical metrics to quantify and assist system-dependent decision-making processes. The latter builds upon symbolic modeling to offer a system-agnostic assessment. The chapter provides an in-depth exploration of quantitative and qualitative methodologies with significant potential to enhance the resilience of layered smart infrastructures. Our exploration covers classical technological aspects (e.g., cascading effects) and socio-technical factors (e.g., human-in-the-loop interaction).

langue originaleAnglais
titreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditeurSpringer Science and Business Media Deutschland GmbH
Pages3-24
Nombre de pages22
Les DOIs
étatPublié - 1 janv. 2025

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14800 LNCS
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

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