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.

中文翻译：

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工业物联网中值得信赖且可靠的基于深度学习的网络攻击检测


利益相关者对工业物联网 (IIoT) 的基本期望是其可靠性和可持续性，以避免在执行关键任务时造成人员伤亡。值得信赖的 IIoT 网络包含基本的安全特征，例如信任、隐私、安全性、可靠性、弹性和安全性。由于协议差异、有限的更新选项以及安全机制的旧适应，传统的安全机制和程序不足以保护这些网络。因此，这些网络需要新的方法来提高信任级别并增强安全和隐私机制。因此，在本文中，我们提出了一种新方法来提高 IIoT 网络的可信度。我们提出在这些网络中进行准确可靠的基于监控和​​数据采集（SCADA）网络的网络攻击检测。所提出的方案将基于深度学习的金字塔循环单元 (PRU) 和决策树 (DT) 与基于 SCADA 的 IIoT 网络相结合。我们还使用集成学习方法来检测基于 SCADA 的 IIoT 网络中的网络攻击。 PRU 和集成 DT 的非线性学习能力解决了不相关特征的敏感性，从而实现了高检测率。所提出的方案在基于 SCADA 的网络生成的 15 个数据集上进行了评估。实验结果表明，所提出的方案优于传统方法和基于机器学习的检测方法。所提出的方案提高了工业物联网网络中的安全性和相关的可信度测量。