Enhancing Security and Reliability in IIoT-SCADA Networks Through Integrated PRU and Decision Tree Models

Stakeholders in the Industrial Web of Things (IIoT) rely on its reliability and longevity to keep people safe as they carry out vital tasks. Integrity, privacy, security, dependability, resilience, and safety are essential components of a reliable IIoT-enabled network. Protocol variations, restricted update choices, and older modifications of security processes render typical security procedures and mechanisms inadequate for protecting these networks. Consequently, these networks need fresh ways to boost security and privacy while also increasing trust level. So, to make IIoT-enabled networks more trustworthy, we provide a new method in this article. We provide a trustworthy method for detecting cyberattacks in SCADA networks that is both accurate and dependable. The proposed approach integrates HoT networks based on SCADA with deep learning-based pyramid recurring units (PRU) & decision trees (DT). To identify cyberattacks in HoT networks that rely on SCADA, we also use an integrated learning approach. High detection rates are achieved by addressing the sensitivity of irrelevant characteristics using the ensemble DT and PRU's nonlinear learning capacity. Fifteen datasets derived from SCADA-based networks are used to assess the proposed method. The suggested strategy outperforms both conventional & machine learning-based detection methods, according to the experimental findings. Networks enabled by the Industrial Internet of Things (IIoT) are made more secure and trustworthy with the help of the suggested scheme.