A network attack detection dataset collected from multiple real-world industrial control systems

Effective detection requires high-quality ICS datasets, yet existing options remain limited in quantity and quality. In this paper, we present the ICS-NAD dataset collected in real-world ICS scenarios with three well-known ICS brands.

It contains two attack traffic sample patterns and covers 20 common ICS attack types. Through feature extraction and labeling, the ICS-NAD dataset provides 60 features with complete labels. We validate its utility using 10 machine learning (ML) and deep learning (DL) classification models.