Intrusion Detection in IIoT Using Machine Learning
Aissétou Ba, Mehdi Adda · Procedia Computer Science · 2024
In the Industrial Internet of Things (IIoT), leveraging Internet of Things (IoT) technologies such as machines, sensors, and software in industrial applications has been instrumental in enhancing productivity. However, the inherent vulnerability of IIoT systems to cyber-attacks poses significant threats to critical infrastructure and security. This paper explores the improvement of IIoT intrusion detection with ML techniques, using supervised models such as Random Forest and Decision Tree on the NF-UNSW-NB15-v2 dataset. SMOTE is applied to balance the data and improve accuracy, recall, and F1-Score. Two approaches, a multiclass classification and a binary classification followed by a multiclass, are evaluated via performance metrics. This study highlights the potential of machine learning to enhance IIoT security and highlights the importance of data balance in intrusion detection systems.