Application of data-driven attack detection framework for secure operation in smart buildings

Elnour, Mariam; Meskin, Nader; Khan, Khaled M.; Jain, Raj

doi:10.1016/j.scs.2021.102816

Cited by 44 publications

(24 citation statements)

References 37 publications

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“…As presented in [1] , the building is a 3-floor office building operating from 6 AM to 6 PM. The floors are labeled A, B, and C and each floor consists of four zones where Zones 1-3 are office rooms and Zone 4 is a hall as shown in Fig.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…The dataset can be used to facilitate validating attack detection and mitigation strategies, benchmarking the performance of different algorithms, and studying the impact of attacks on the HVAC system. As described in Table 7 , four code files are provided as supplementary materials for training machine learning-based detection models using the Isolation Forest algorithm [1] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Given the limitations on accessing a real HVAC system for research purposes and the unavailability of public labeled datasets to investigate the cybersecurity of HVAC systems, this paper presents a dataset of a 12-zone HVAC system that was collected from a simulation model using the Transient System Simulation Tool (TRNSYS). It aims to promote and support the research in the field of cybersecurity of HVAC systems in smart buildings [1] by facilitating the validation of attack detection and mitigation strategies, benchmarking the performance of different data-driven algorithms, and studying the impact of attacks on the HVAC system.…”

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confidence: 99%

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HVAC system attack detection dataset

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The importance of the security of building management systems (BMSs) has increased given the advances in the technologies used. Since the Heating, Ventilation, and Air Conditioning (HVAC) system in buildings accounts for about 40% of the total energy consumption, threats targeting the HVAC system can be quite severe and costly. Given the limitations on accessing a real HVAC system for research purposes and the unavailability of public labeled datasets to investigate the cybersecurity of HVAC systems, this paper presents a dataset of a 12-zone HVAC system that was collected from a simulation model using the Transient System Simulation Tool (TRNSYS). It aims to promote and support the research in the field of cybersecurity of HVAC systems in smart buildings [1] by facilitating the validation of attack detection and mitigation strategies, benchmarking the performance of different data-driven algorithms, and studying the impact of attacks on the HVAC system.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

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confidence: 99%

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HVAC system attack detection dataset

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“…Moving on, in Elnour et al. ( 2021 ), Elnour et al propose an SSL-based data-driven attack detection scheme in HVAC systems to promote security in intelligent buildings. This approach has been developed using an isolation forest and two ML models, i.e.…”

Section: Overview Of Ai-big Data Analytic Frameworkmentioning

confidence: 99%

“…In Elnour et al. ( 2021 ), an attack detection framework for false data injection was proposed for a multi-zone HVAC system in office buildings utilizing an isolation forest (IF) algorithm. The operational data of the system’s sensor and control command signals were used to develop the detection model using semi-supervised learning.…”

Section: Overview Of Ai-big Data Analytic Frameworkmentioning

confidence: 99%

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

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In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

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