An analytics-driven framework for securing industrial IoT-Enabled Supply Chain Management Systems

In today's dynamic technological environment, the integration of IoT into Supply Chain Management Systems (SCMS) has significantly enhanced functionality, visibility, and decision-making. However, integrating Industrial-IoT (IIoT) with Supply Chain Networks (SCN) is an equally significant security concern because of interconnected systems amplified exposure and complexity. This study proposes an original Intrusion Detection System (IDS) framework based on the Staked Ensemble Model appropriate for IIoT-Enabled SCMS. A stacked ensemble model-based IDS framework operates as a novel solution to protect IIoT-Enabled SCMS. A multilayered system unites Extreme Gradient Boosting (XGBoost) with Light Gradient Boosting Machine (LightGBM) along with Deep Neural Networks (DNN) as a stacked ensemble design to enable decentralized and secure collaborative learning across the supply chain network and protect user data and maintain system stability as well as network reliability. On the other hand, Synthetic Minority Oversampling Technique (SMOTE) and Principal Component Analysis (PCA) are established techniques, and our contribution is in optimizing the application of those for IIoT traffic. We tackle the class imbalance in intrusion data with SMOTE to better detect rare attacks and to use PCA to reduce the high dimensions of feature space for less computational effort and more efficient pattern recognition. To meet the requirements of the IIoT use cases, these preprocessing techniques are effectively embedded in the framework. Moreover, the proposed modular IDS architecture, the curation and fine tuning of the various learners, and the approach to full validation are all novel. We rigorously evaluate the model under K-Fold Cross Validation using the IoT-23 dataset and prove superior detection performance when compared to state-of-the-art approaches. Specifically, this research contributes a scalable and efficient IDS for an IIoT scenarios such as real-world IIoT enabled SCMS, which improves security analytics and facilitates network defense in key operational functionalities such as low data rates, low computational resources availability and restricted communication over the year.