This paper presents a novel predictive approach designed for bolstering cybersecurity measures in the wireless sensor networks (WSNs) of Industry 4.0. By harnessing the capabilities of both machine learning and deep learning algorithms, our proposed system seeks to significantly enhance the security protocols deployed within these networks. While the current system relies on traditional algorithms such as Decision Trees, Random Forests, Multi-layer Perceptrons (MLP), and Logistic Regression (LR), our research advocates for a paradigm shift towards more advanced techniques. Specifically, we advocate for the integration of Stacking Classifier, XGBoost, and Adaboost Classifier to fortify the intrusion detection and prevention mechanisms. Through a series of comprehensive experiments, we substantiate the efficacy of our proposed approach in combating cyber threats. By doing so, we contribute towards safeguarding the integrity of critical data transmitted across wireless sensor networks in the context of Industry 4.0.

Keywords: Cybersecurity, Intrusion Detection, Prevention, Industry 4.0, Wireless Sensor Networks, Machine Learning, Deep Learning, Stacking Classifier, XGBoost, Adaboost Classifier.