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.