Machine learning applications in the resilience of interdependent critical infrastructure systems—A systematic literature review

IF 4.1 3区工程技术 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Critical Infrastructure Protection Pub Date : 2023-12-04 DOI:10.1016/j.ijcip.2023.100646

Basem A. Alkhaleel

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引用次数: 0

Abstract

The resilience of interdependent critical infrastructure systems (ICISs) is critical for the functioning of society and the economy. ICISs such as power grids and telecommunication networks are complex systems characterized by a wide range of interconnections, and disruptions to such systems can cause significant socioeconomic losses. This vital role requires the adaptation of new tools and technologies to improve the modeling of such complex systems and achieve the highest levels of resilience. One of the trending tools in many research fields to model complex systems is machine learning (ML). In this article, a systematic review of the literature on ML applications in ICISs resilience is conducted, considering the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), to address the lack of knowledge and scattered research articles on the topic. The main objective of this systematic review is to determine the state of the art of ML applications in the area of ICISs resilience engineering by exploring the current literature. The results found were summarized and some of the future opportunities for ML in ICISs resilience applications were outlined to encourage resilience engineering communities to adapt and use ML for various ICISs applications and to utilize its potential.

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机器学习在相互依存的关键基础设施系统复原力中的应用--系统性文献综述

相互依存的关键基础设施系统（ICIS）的复原力对社会和经济的运行至关重要。电网和电信网络等关键基础设施系统是以广泛互连为特征的复杂系统，这些系统的中断会造成重大的社会经济损失。这种重要作用要求采用新的工具和技术来改进这类复杂系统的建模，并实现最高水平的复原力。在许多研究领域，机器学习（ML）是复杂系统建模的趋势工具之一。本文根据《系统综述和元分析首选报告项目》（PRISMA）的规定，对有关 ML 应用于 ICIS 复原力的文献进行了系统综述，以解决有关该主题的知识缺乏和研究文章分散的问题。本系统综述的主要目的是通过探索当前文献，确定智能语言在集成电路信息系统复原力工程领域的应用现状。对所发现的结果进行了总结，并概述了 ML 在 ICISs 复原力应用中的一些未来机遇，以鼓励复原力工程界在 ICISs 的各种应用中调整和使用 ML，并利用其潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Critical Infrastructure Protection COMPUTER SCIENCE, INFORMATION SYSTEMS-ENGINEERING, MULTIDISCIPLINARY

CiteScore

8.90

自引率

5.60%

发文量

审稿时长

>12 weeks

期刊介绍： The International Journal of Critical Infrastructure Protection (IJCIP) was launched in 2008, with the primary aim of publishing scholarly papers of the highest quality in all areas of critical infrastructure protection. Of particular interest are articles that weave science, technology, law and policy to craft sophisticated yet practical solutions for securing assets in the various critical infrastructure sectors. These critical infrastructure sectors include: information technology, telecommunications, energy, banking and finance, transportation systems, chemicals, critical manufacturing, agriculture and food, defense industrial base, public health and health care, national monuments and icons, drinking water and water treatment systems, commercial facilities, dams, emergency services, nuclear reactors, materials and waste, postal and shipping, and government facilities. Protecting and ensuring the continuity of operation of critical infrastructure assets are vital to national security, public health and safety, economic vitality, and societal wellbeing. The scope of the journal includes, but is not limited to: 1. Analysis of security challenges that are unique or common to the various infrastructure sectors. 2. Identification of core security principles and techniques that can be applied to critical infrastructure protection. 3. Elucidation of the dependencies and interdependencies existing between infrastructure sectors and techniques for mitigating the devastating effects of cascading failures. 4. Creation of sophisticated, yet practical, solutions, for critical infrastructure protection that involve mathematical, scientific and engineering techniques, economic and social science methods, and/or legal and public policy constructs.