IWCS-Dataset: Simulation Dataset for Cyberattack Analysis in IIoT Wireless Sensor Networks

The IWCS-Dataset is a simulation-based dataset for cyberattack analysis in Industrial Internet of Things (IIoT) Wireless Sensor Networks. The dataset was generated using OMNeT++/INET with IEEE 802.15.4, RPL, and UDP traffic, considering multiple network topologies and attack scenarios. The curated dataset contains 20,331 records and 16 attributes. It includes four grid-based topologies, namely Grid_36, Grid_49, Grid_64, and Grid_100, and five operational/security scenarios: Normal, Flooding, Blackhole, Wormhole, and Manipulated Backoff. The dataset provides network performance and security-related metrics such as Packet Delivery Ratio (PDR), average delay, throughput, energy consumption, RSSI, RPL control-message indicators, and attack labels. These data can support reproducibility, comparative performance analysis, and the development of machine learning or probabilistic models for cyberattack detection in IIoT/WSN environments. Due to the large size of the complete OMNeT++ raw simulation outputs, this repository includes the final curated CSV dataset, metadata, documentation, scripts, a raw file manifest, and representative raw OMNeT++ samples in .sca, .vec, and .vci formats. The representative raw samples cover selected executions, including runs 0 and 999, across the evaluated scenarios and topologies. This dataset was developed as part of a master's research project at the Escola Politécnica da Universidade de São Paulo.