Cybersecurity Intrusion Detection in Industry 4.0 WSN’s Using Ml/Dl
Keywords:
Industry 4.0, Wireless Sensor Networks (WSNs), Cybersecurity, Intrusion Detection, Machine Learning, Deep Learning, Stacking Classifier, XGBoost, AdaBoostAbstract
Industry 4.0 is revolutionizing manufacturing and industrial processes by integrating intelligent technologies, including Wireless Sensor Networks (WSNs). However, this increased reliance on connected devices exposes these networks to cyber threats. To address this vulnerability, this article proposes an intrusion detection system for Industry 4.0 WSNs based on Machine Learning (ML) and Deep Learning (DL). Specifically, we employ ensemble learning methods, such as Stacking Classifier, XGBoost, and AdaBoost, to optimize detection accuracy and minimize false alarms. By applying our methods to real-world data, we demonstrate superior performance compared to existing intrusion detection models.
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Articles in this journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. This license permits others to copy, distribute, and adapt the work, provided it is for non-commercial purposes, and the original author and source are properly credited.
