False Data Injection Attack Dataset for Industrial Internet of Things

Training and testing the accuracy of machine learning or deep learning based on cybersecurity applications requires gathering and analyzing various sources of data including the Internet of Things (IoT), especially Industrial IoT (IIoT). Minimizing high-dimensional spaces and choosing significant features and assessments from various data sources remain significant challenges in the investigation of those data sources. The research study introduces an innovative IIoT system dataset called UKMNCT_IIoT_FDIA, that gathered network, operating system, and telemetry data. The datasets' initial statistical analysis shows that they can be used to assess cybersecurity applications like threat intelligence, intrusion detection, adversarial machine learning, deep learning, and privacy-preserving models.

在基于网络安全应用的机器学习或深度学习模型的训练与测试过程中，需搜集并分析包括物联网（IoT）在内的多种数据来源，尤其是工业物联网（IIoT）。在探究这些数据源的过程中，降低高维空间并从各类数据源中选取显著特征与评估指标仍是一项重大挑战。本研究引入了一种创新的IIoT系统数据集，称为UKMNCT_IIoT_FDIA，该数据集汇集了网络、操作系统和遥测数据。数据集的初步统计分析表明，它们可用于评估诸如威胁情报、入侵检测、对抗性机器学习、深度学习和隐私保护模型等网络安全应用。