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.

针对网络安全应用场景下的机器学习或深度学习模型开展精度训练与测试，需收集并分析多源数据，其中涵盖物联网（Internet of Things, IoT），尤其是工业物联网（Industrial IoT, IIoT）相关数据。从多源数据中压缩高维特征空间、筛选有效特征并开展评估，仍是此类数据研究过程中面临的核心挑战。本研究提出一款新型工业物联网系统数据集，命名为UKMNCT_IIoT_FDIA，该数据集采集了网络数据、操作系统数据与遥测数据。对该数据集开展的初步统计分析表明，其可用于评估威胁情报（Threat Intelligence）、入侵检测（Intrusion Detection）、对抗式机器学习（Adversarial Machine Learning）、深度学习（Deep Learning）以及隐私保护模型（Privacy-Preserving Models）等各类网络安全应用。