Enhancing I-IoT against malicious intrusions through XGBoost

Abstract

Industrial internet of things (I-IoT) is the core of industry 4.0 that refers to the fourth industrial revolution, characterized by the integration of digital technologies into various aspects of industrial processes. It is a paradigm shift that combines traditional manufacturing with the latest smart technology. Ensuring the security of these devices is crucial, acting as a key element for the smooth flow of operations within the organization. Moreover, building a strong cybersecurity framework not only protects against potential threats but also nurtures a climate of trust and dependability in industrial processes. However, traditional security methods struggle due to the limited storage and computing capacity of I-IoT devices. On the other hand, machine learning techniques, specifically employing intrusion detection S, offer improved efficacy in identifying both unknown and zero-day attacks. To address this challenge, we integrate an eXtreme gradient boosting (XGBoost) model, known for its learning capabilities, enabling efficient detection of diverse attacks without encountering overfitting problems. We evaluated the reliability, efficiency, and flexibility of our model by utilizing two diverse datasets, incorporating network, IoT, and I-IoT traffic. The first dataset is considered medium in size, while the second is characterized as large and complex. Our XGBoost model showcased remarkable efficiency, outperforming previous studies across key evaluation metrics accuracy, F1-score, recall, and precision. It displayed exceptional proficiency in identifying various attack types within both medium and complex datasets, yielding consistently high-performance outcomes. Notably, it achieved perfect scores of 100% across all evaluation metrics for X-IIoTID and an impressive 99.88% for Edge-IIoTSet.