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

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.