IIoT-VulnSet: A Comprehensive Dataset for Vulnerabilities and Attacks in Industrial IoT Protocols

The Industrial Internet of Things (IIoT) improves productivity and enables real-time monitoring across industrial systems. Nevertheless, this increased connectivity broadens the attack surface, exposing critical infrastructure controlled by IIoT to a wider range of cyber threats. Vulnerabilities in IIoT systems have serious consequences, as a flaw can disrupt production, compromise safety, or affect the stability of critical infrastructures. Consequently, there is a pressing need for efficient vulnerability assessment methods to identify and mitigate vulnerabilities in IIoT systems. A comprehensive dataset capturing the vulnerabilities of IIoT protocols is therefore indispensable, as it establishes the basis for developing and advancing robust vulnerability assessment methods. Nonetheless, existing publicly available datasets exhibit narrow protocol diversity and limited representation of vulnerabilities. This deficiency significantly constrains the development of rigorous vulnerability assessment methods for IIoT systems. To bridge this gap, we present IIoT-VulnSet, a comprehensive dataset encompassing vulnerabilities in multiple IIoT communication protocols, including Modbus, DNP3, MQTT, OPC UA, and S7 Comm. IIoT-VulnSet offers heterogeneity by incorporating diverse industrial protocols and enhances vulnerability representation by explicitly linking attack vectors to the corresponding exploited vulnerabilities, thereby enabling exhaustive and in-depth vulnerability assessment across diverse IIoT protocols. We further assessed the dataset’s utility by applying deep learning and traditional machine learning models, demonstrating its effectiveness in supporting the design and evaluation of advanced IIoT vulnerability assessment methods.