The continued growth in the deployment of Internet-of-Things (IoT) devices has been fueled by the increased connectivity demand, particularly in industrial environments. However, this has led to an increase in the number of network related attacks due to the increased number of potential attack surfaces. Industrial IoT (IIoT) devices are prone to various network related attacks that can have severe consequences on the manufacturing process as well as on the safety of the workers in the manufacturing plant. One promising solution that has emerged in recent years for attack detection is Machine learning (ML). More specifically, ensemble learning models have shown great promise in improving the performance of the underlying ML models. Accordingly, this paper proposes a framework based on the combined use of Bayesian Optimization-Gaussian Process (BO-GP) with an ensemble tree-based learning model to improve the performance of intrusion and attack detection in IIoT environments. The proposed framework's performance is evaluated using the Windows 10 dataset collected by the Cyber Range and IoT labs at University of New South Wales. Experimental results illustrate the improvement in detection accuracy, precision, and F-score when compared to standard tree and ensemble tree models.

翻译：物联网设备部署的持续增长得益于日益增长的连接需求，尤其是在工业环境中。然而，这也导致潜在攻击面的增加，进而引发了网络相关攻击数量的上升。工业物联网设备易受各种网络攻击，这些攻击可能对制造过程及工厂工人的安全造成严重后果。近年来，一种有前景的攻击检测解决方案是机器学习。具体而言，集成学习模型在提升基础机器学习模型性能方面展现出巨大潜力。据此，本文提出一种基于贝叶斯优化-高斯过程与集成树学习模型联合使用的框架，以提升工业物联网环境中入侵与攻击检测的性能。该框架的性能通过新南威尔士大学网络靶场与物联网实验室收集的Windows 10数据集进行评估。实验结果表明，与标准树模型和集成树模型相比，该框架在检测准确率、精确率和F分数上均有提升。