HVAC system - Attack-detection

Published: 3 June 2021| Version 1 | DOI: 10.17632/p63m3jrx9n.1
Contributors:
Mariam Elnour,
Nader Meskin,
Khaled Khan,
Raj Jain

Description

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

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