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

Abstract

Construction 4.0 integrates digital technologies that increase vulnerability to cyber threats. A dedicated cyber risk assessment framework is essential for proactive risk mitigation. However, existing studies on this subject within the construction sector are scarce, with most discussions still in the preliminary stages. This study introduces a cyber risk assessment framework that integrates machine learning techniques, pioneering a data-driven approach to quantitatively assess cyber risks while considering industry-specific vulnerabilities. The framework builds on over 20 literature reviews related to construction cybersecurity and semi-structured interviews with two industry experts, ensuring both rigor and alignment with practical industrial needs. This study also addresses the challenges of data collection and proposes potential solutions, such as a standardized data collection format with preset fields that computers can automatically populate using data from construction companies. Additionally, the framework proposes dynamic machine learning models that adjust based on new data, facilitating continuous risk monitoring tailored to industry needs. Furthermore, this study explores the potential of advanced language models in cybersecurity management, positioning them as intelligent cybersecurity consultants that provide answers to security inquiries. Overall, this study develops a conceptual machine learning framework aimed at creating a robust, off-the-shelf cyber risk management system for industry practitioners.