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.IWCS-Dataset是一款基于仿真的工业物联网（Industrial Internet of Things, IIoT）无线传感器网络（Wireless Sensor Networks, WSN）网络攻击分析数据集。该数据集依托OMNeT++/INET仿真框架，结合IEEE 802.15.4协议、RPL路由协议与UDP流量生成，覆盖多种网络拓扑与攻击场景。 经整理后的数据集共包含20331条记录与16项属性，涵盖四种网格型拓扑结构：Grid_36、Grid_49、Grid_64及Grid_100，同时包含五种运行/安全场景：正常（Normal）、泛洪攻击（Flooding）、黑洞攻击（Blackhole）、虫洞攻击（Wormhole）与退避操纵攻击（Manipulated Backoff）。 本数据集提供与网络性能及安全相关的多项指标，包括包投递率（Packet Delivery Ratio, PDR）、平均延迟、吞吐量、能耗、接收信号强度指示（Received Signal Strength Indicator, RSSI）、RPL控制消息指标以及攻击标签。此类数据可用于支撑IIoT/WSN环境下网络攻击检测相关的可复现性研究、对比性能分析，以及机器学习或概率模型的开发工作。 鉴于完整OMNeT++原始仿真输出体量较大，本仓库仅提供整理后的最终CSV格式数据集、元数据、说明文档、脚本文件、原始文件清单，以及.sca、.vec与.vci格式的代表性OMNeT++原始样本。该代表性原始样本覆盖评估场景与拓扑下的部分执行实例，包含运行编号0与999的任务。 本数据集为圣保罗大学理工学院（Escola Politécnica da Universidade de São Paulo）硕士研究项目的成果之一。