Modern construction projects are vulnerable to cyber-attacks due to insufficient attention to cybersecurity. Cyber risks in construction projects are not fully recognized, and the relevant literature is limited. To address this gap, the capabilities of a language model were leveraged to analyze extensive text, tailored to identify cyber risks. The model was trained using a curated corpus related to construction cybersecurity, enhanced by Supervised Fine-Tuning and Reinforcement Learning from Human Feedback techniques. The findings demonstrate advancements in the model's ability to understand cybersecurity and generate responses to cybersecurity questions. Using this model, a prioritized checklist of cyber risks across project phases was developed, establishing a new industry benchmark. This checklist can be utilized by various groups, including project managers and risk analysts. The model allows for updates with new data, ensuring the checklist remains current. The upgraded model holds significant promise for industry-wide applications, serving as an intelligent cybersecurity consultant.

中文翻译：

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使用语言模型增强建筑行业的网络风险识别


由于对网络安全重视不够，现代建筑项目很容易受到网络攻击。建设项目中的网络风险尚未得到充分认识，相关文献也有限。为了解决这一差距，利用语言模型的功能来分析大量文本，以识别网络风险。该模型使用与建筑网络安全相关的精选语料库进行训练，并通过监督微调和人类反馈技术的强化学习进行增强。研究结果表明，该模型在理解网络安全和对网络安全问题做出响应的能力方面取得了进步。使用该模型，制定了跨项目阶段的网络风险优先清单，建立了新的行业基准。该清单可供各种团体使用，包括项目经理和风险分析师。该模型允许使用新数据进行更新，确保清单保持最新状态。升级后的模型为全行业应用带来了巨大的希望，可以作为智能网络安全顾问。