TY - JOUR
T1 - Cyber security threat modeling in the AEC industry
T2 - An example for the commissioning of the built environment
AU - Mantha, Bharadwaj
AU - García de Soto, Borja
AU - Karri, Ramesh
N1 - Funding Information:
We dedicate this manuscript to the memory of Pamela Sklar, whose guidance and wisdom we miss daily. We strive to continue her legacy of thoughtful, innovative, and collaborative science. Data were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881 and R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories and the NIMH Human Brain Collection Core. CMC Leadership: P. Sklar, J. Buxbaum (Icahn School of Medicine at Mount Sinai), B. Devlin, D. Lewis (University of Pittsburgh), R. Gur, C.-G. Hahn (University of Pennsylvania), K. Hirai, H. Toyoshiba (Takeda Pharmaceuticals Company Limited), E. Domenici, L. Essioux (F. Hoffman-La Roche Ltd), L. Mangravite, M. Peters (Sage Bionetworks), T. Lehner, B. Lipska (NIMH). ROSMAP study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152, the Illinois Department of Public Health, and the Translational Genomics Research Institute. The iPSYCH-GEMS team acknowledges funding from the Lundbeck Foundation (grant no. R102-A9118 and R155-2014-1724), the Stanley Medical Research Institute, an Advanced Grant from the European Research Council (project no. 294838), the Danish Strategic Research Council the Novo Nordisk Foundation for supporting the Danish National Biobank resource, and grants from Aarhus and Copenhagen Universities and University Hospitals, including support to the iSEQ Center, the GenomeDK HPC facility, and the CIRRAU Center. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on September 5, 2016. BrainSpan: Atlas of the Developing Human Brain (Internet). Funded by ARRA Awards 1RC2MH089921-01, 1RC2MH090047-01, and 1RC2MH089929-01. H.K.I. was supported by R01 MH107666-01.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/3
Y1 - 2021/3
N2 - Digitalization and automation are making the architecture, engineering, and construction (AEC) industry more vulnerable to cyberattacks. Existing literature suggests that industry-specific studies need to be conducted. The work presented in this study shows a preliminary cybersecurity threat model relevant to the AEC industry. To that end, threat models for each of the life cycle phases are proposed. The feasibility of the proposed approach is illustrated with an example from the commissioning phase of a building, which includes an autonomous robotic system to collect data as a possible countermeasure. The suggested countermeasure shows promise to address some of the cybersecurity challenges faced in the building certification and commissioning process. The results show that the likelihood of detecting rogue sensors increases with additional constraints in the monitoring robot, such as minimum and maximum distance. The illustrative models suggest that the proposed framework will help to address the safety and cyber security of stakeholders and systems during crucial phases of construction projects.
AB - Digitalization and automation are making the architecture, engineering, and construction (AEC) industry more vulnerable to cyberattacks. Existing literature suggests that industry-specific studies need to be conducted. The work presented in this study shows a preliminary cybersecurity threat model relevant to the AEC industry. To that end, threat models for each of the life cycle phases are proposed. The feasibility of the proposed approach is illustrated with an example from the commissioning phase of a building, which includes an autonomous robotic system to collect data as a possible countermeasure. The suggested countermeasure shows promise to address some of the cybersecurity challenges faced in the building certification and commissioning process. The results show that the likelihood of detecting rogue sensors increases with additional constraints in the monitoring robot, such as minimum and maximum distance. The illustrative models suggest that the proposed framework will help to address the safety and cyber security of stakeholders and systems during crucial phases of construction projects.
KW - AEC industry
KW - Building commissioning
KW - Construction 4.0
KW - Construction automation
KW - Cyber-physical system
KW - Cybersecurity
KW - Mobile robots
KW - Threat modeling
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U2 - 10.1016/j.scs.2020.102682
DO - 10.1016/j.scs.2020.102682
M3 - Article
AN - SCOPUS:85099063851
SN - 2210-6707
VL - 66
JO - Sustainable Cities and Society
JF - Sustainable Cities and Society
M1 - 102682
ER -