HVAC system - Attack-detection

The dataset can be used to study the cybersecurity aspect of the HVAC system by evaluating the different attack detection and mitigation strategies. The dataset was collected from a simulation model of a 3-floor, 12-zone HVAC system for cooling using the Transient System Simulation Tool (TRNSYS), which is a graphical software environment for simulating a dynamical system. It consists of three logs: Dataset log 1 contains normal operational data collected for four months, Dataset log 2 represents normal operational data collected for 20 days. Dataset log 3 consists of the normal and attack data of 16 different attacks. It consists of 65 features: the hour of the year, the hour of the day, the temperature sensor measurements, the control signals, the control system's setpoints, the zones' thermal comfort indices, and the total estimated power usage of the HVAC system. Four files are provided as supplementary materials for training machine learning-based detection models using the Isolation Forest algorithm [1]. The details of the supplementary codes are as follows: File "HVAC - IF Training.ipynb" is for developing an attack detection model using Isolation Forest on the raw data, File "HVAC - PCA-IF Training.ipynb" is for developing an attack detection model using Isolation Forest on the data features extracted using Principal Component Analysis (PCA), File "HVAC - 1D CNN Training.ipynb" is for developing a feature extraction model using 1D Convolutional Neural Network (1D CNN), and File "HVAC - 1D CNN-IF Training.ipynb" is for developing an attack detection model using Isolation Forest on the data features extracted using the 1D CNN model.

For more information about the dataset refer to the following publications:

[1] Elnour, M., Meskin, N., Khan, K., & Jain, R. (2021). Application of data-driven attack detection framework for secure operation in smart buildings. Sustainable Cities and Society, 69, 102816. https://doi.org/10.1016/j.scs.2021.102816

[2] Elnour, M., Meskin, N., Khan, K., & Jain, R. (2021). HVAC System Attack Detection Dataset. Data in Brief, 107166. https://doi.org/10.1016/j.dib.2021.107166

* This dataset was supported by the Qatar National Research Fund (a member of the Qatar Foundation) under NPRP Grants number 10-0206-170360 and the Open Access funding was provided by the Qatar National Library

本数据集可用于研究暖通空调系统（HVAC）的网络安全方面，通过评估不同的攻击检测与缓解策略。数据集由3层、12区的HVAC系统模拟模型收集而来，该模拟模型采用瞬态系统仿真工具（TRNSYS）进行仿真，TRNSYS是一款用于模拟动态系统的图形化软件环境。数据集包含三份日志：数据集日志1包含四个月收集的正常运行数据，数据集日志2代表20天内收集的正常运行数据。数据集日志3包含了16种不同攻击的正常与攻击数据。数据集包含65个特征：年度小时数、日小时数、温度传感器测量值、控制信号、控制系统设定点、区域热舒适度指数以及HVAC系统的总预估功耗。为训练基于隔离森林算法的机器学习检测模型，提供了四份补充材料。补充代码的详细信息如下：文件"HVAC - IF Training.ipynb"用于开发基于原始数据的攻击检测模型，文件"HVAC - PCA-IF Training.ipynb"用于开发基于主成分分析（PCA）提取的特征的攻击检测模型，文件"HVAC - 1D CNN Training.ipynb"用于开发使用一维卷积神经网络（1D CNN）的特征提取模型，而文件"HVAC - 1D CNN-IF Training.ipynb"则用于开发基于一维CNN模型提取的特征的攻击检测模型。关于数据集的更多信息，请参考以下出版物：[1] Elnour, M.，Meskin, N.，Khan, K.，& Jain, R.（2021）。在智能建筑中应用数据驱动攻击检测框架以确保安全运行。可持续城市与社区，69，102816。https://doi.org/10.1016/j.scs.2021.102816。[2] Elnour, M.，Meskin, N.，Khan, K.，& Jain, R.（2021）。HVAC系统攻击检测数据集。数据简报，107166。https://doi.org/10.1016/j.dib.2021.107166。本数据集由卡塔尔国家研究基金（卡塔尔基金会成员）提供资助，资助编号为NPRP Grants number 10-0206-170360，开放获取资金由卡塔尔国家图书馆提供。