Abstract

     A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics, such as trust, privacy, security, reliability, resilience, and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this article, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep-learning-based pyramidal recurrent units (PRU) and decision tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The nonlinear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on 15 datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.

Introduction

     THE Industrial Internet of Things (IIoT) is a pervasive network that connects a diverse set of smart appliances in the industrial environment to deliver various intelligent services. In IIoT networks, a significant amount of industrial control systems (ICSs) premised on supervisory control and data acquisition (SCADA) are linked to the corporate network through the Internet [1]. Typically, these SCADA-based IIoT networks consist of a large number of field devices [2], for instance, intelligent electronic devices, sensors, and actuators, connected to an enterprise network via heterogeneous communications [3]. This integration provides the industrial networks and systems with supervision and a lot of flexibility and agility [2]–[۴], resulting in greater production and resource efficiency. On the other hand, this integration exposes SCADA-based IIoT networks to serious security threats and vulnerabilities, posing a significant danger to these networks and the trustworthiness of the systems [5]. The trustworthiness of an IIoT-enabled system ensures that it performs as expected while meeting a variety of security requirements, including trust, security, safety, reliability, resilience, and privacy [6]–[۸]. Fig. 1 depicts the fundamental aspects of trustworthiness in an IIoT-enabled network. The basic goal of the IIoT-enabled system is to increase trustworthiness by safeguarding identities, data, and services, and therefore to secure SCADA-based IIoT networks from cybercriminals [8], [9].

Conclusion

     The ability to protect SCADA-based IIoT networks against cyberattacks increases their trustworthiness. The existing security methods along with machine learning algorithms were inefficient and inaccurate for protecting IIoT networks. In this article, we proposed a cyberattacks detection mechanism using enhanced deep and ensemble learning in a SCADA-based IIoT network. The proposed mechanism is reliable and accurate because an ensemble detection model was built using a combination of the PRU and the DT. The proposed method was evaluated across 15 datasets generated from a SCADA-based network, and a considerable increase in terms of classification accuracy was obtained. Compared to state-of-the-art techniques, the obtained outcomes of our method exhibited a good balance between reliability, trustworthiness, classification accuracy, and model complexity, resulting in improved performance.

     In the future, we will employ more powerful deep learning models to further improve trustworthiness by detecting cyberattacks accurately. In addition, we will try to formulate and assess its performance in real-world scenarios. Also, we will work on the selection of optimal features in scenarios when the features are not sufficient.