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.

提出了一种基于贝叶斯优化 - 高斯过程（BO-GP）和集成树状学习模型的框架，以提高工业物联网环境中入侵和攻击检测的性能。实验结果显示，与标准树模型和集成树模型相比，该框架在检测精度、准确率和 F-score 方面有所提高。