A preliminary SWOT evaluation for the applications of ML to Cyber Risk Analysis in the Construction Industry

Abstract

Construction 4.0 is driving construction towards a data-centered industry. Construction firms manage significant amounts of valuable digital information, making them the target of cyberattacks, which not only compromise stored information but could cause severe harm to cyber-physical systems, personnel, and products. Therefore, it is critical to conduct cyber risk analyses to manage construction information assets to ensure their confidentiality, integrity, and availability. Traditional risk analysis methodologies like Fault Tree Analysis have limitations in dealing with the rapidly evolving cyber risks. As an alternative, Machine Learning (ML) methods are finding their way into the risk analysis field. ML models developed for cybersecurity purposes can learn from past results to make reliable predictions while removing the laboriousness of the traditional risk analysis. This article reviews ML techniques used for cyber risk analysis in different industries in recent years. Based on that, we investigate how ML techniques could be used for cyber risk analysis. Afterward, a SWOT analysis is conducted to identify the Strengths, Weaknesses, Opportunities, and Threats regarding the applications of ML in cyber risk analysis in the construction industry, and recommendations to address the weaknesses and threats are presented. Finally, future research areas using ML to prevent cyberattacks in the construction industry are proposed.

Cyber Risk Assessment Framework for the Construction Industry Using Machine Learning Techniques

“…The construction industry, like many other sectors, is vulnerable to IT threats due to its reliance on digital technologies and networked systems in project management, design software, and operational processes [43]. Different standards, such as NIST [11] and PAS 1192 [44], have been explored to identify IT vulnerabilities relevant to the construction industry. Our analysis categorized these vulnerabilities into four main categories, detailed in Table 2.…”

“…ML not only enhances prediction accuracy through advanced statistical techniques but also manages the industry's complex datasets more effectively, extracting insights from diverse data sources such as project timelines, contracts, and work logs-tasks that traditional methods may struggle with over short periods. This enables project managers to make more informed decisions [11]. methods, such as threat modeling and fault tree analysis, demand significant human input and are time-consuming.…”