Abstract
The trustworthiness of an industrial Internet of Things (IIoT) network is an important stakeholder expectation. Maintaining the trustworthiness of such a network is crucial to void the loss of lives. A trustworthy IIoT system combines the security characteristics of IT trustworthiness-safety, security, privacy, reliability, and resilience. Conventional security tools and techniques are not enough to safeguard the IIoT platform due to the difference in protocols, limited upgrade opportunities, mismatch in protocols, and older versions of the operating system used in the industrial system. In this article, we propose to improve the trustworthiness of an IIoT network [i.e., supervisory control and data acquisition (SCADA) network] through a reliable and salable cyberattack detection model. In particular, an ensemble-learning model based on the combination of a random subspace (RS) learning method with random tree (RT) is proposed for detecting cyberattacks of SCADA by using the network traffics from the SCADA-based IIoT platform. The novelty of the proposed model is that it uses the industrial protocol-based network traffic and the RS to solve the sensitivity of irrelevant features and ensemble RT to reduce the overfitting problem, thereby constructs a detection engine based on industrial protocols and achieves high detection rates. The proposed model has been tested over 15 datasets of the SCADA network. Experimental results reveal that the proposed model outperforms conventional detection techniques and, thus, improves the security and related measure of the trustworthiness of the IIoT platform.