BACGraph: Automatic Extraction of Object Relationships in the BACnet Protocol

Herson Esquivel-Vargas; Marco Caselli; Andreas Peter

doi:10.1109/DSN-S52858.2021.00029

BACGraph: Automatic Extraction of Object Relationships in the BACnet Protocol

Herson Esquivel-Vargas, Marco Caselli, Andreas Peter

Semantics, Cybersecurity & Services

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

30 Downloads (Pure)

Abstract

This work presents BACGRAPH, a tool that extracts relationships among configuration parameters of Building Automation and Control Systems (BACSs) implemented using the BACnet protocol (ISO 16484-5). BACnet models these configuration parameters as object data structures comprised of multiple properties, some of which contain references to other objects. Given the regular exchange of objects among devices, we leverage these explicit references to build a graph of BACnet objects exclusively from network traffic. We tested BACGRAPH using traffic collected from a real building located at the University of Twente. After analyzing 66.8 hours of traffic, the resulting graph is comprised of 13,733 nodes and 3,169 edges. Such a graph improves the system visibility that BACS administrators have over their infrastructure, which is crucial for troubleshooting and security.

Original language	English
Title of host publication	2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks
Subtitle of host publication	Supplemental Volume (DSN-S)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	45-48
Number of pages	4
ISBN (Electronic)	978-1-6654-3566-6
ISBN (Print)	978-1-6654-0252-1
DOIs	https://doi.org/10.1109/DSN-S52858.2021.00029
Publication status	Published - 2 Sept 2021
Event	51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2021 - Taipei, Taiwan Duration: 21 Jun 2021 → 24 Jun 2021 Conference number: 51

Conference

Conference	51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2021
Abbreviated title	DSN
Country/Territory	Taiwan
City	Taipei
Period	21/06/21 → 24/06/21

Keywords

n/a OA procedure

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/DSN-S52858.2021.00029

Cite this

@inproceedings{cbd09ff295994416ac96171569d8f008,

title = "BACGraph: Automatic Extraction of Object Relationships in the BACnet Protocol",

abstract = "This work presents BACGRAPH, a tool that extracts relationships among configuration parameters of Building Automation and Control Systems (BACSs) implemented using the BACnet protocol (ISO 16484-5). BACnet models these configuration parameters as object data structures comprised of multiple properties, some of which contain references to other objects. Given the regular exchange of objects among devices, we leverage these explicit references to build a graph of BACnet objects exclusively from network traffic. We tested BACGRAPH using traffic collected from a real building located at the University of Twente. After analyzing 66.8 hours of traffic, the resulting graph is comprised of 13,733 nodes and 3,169 edges. Such a graph improves the system visibility that BACS administrators have over their infrastructure, which is crucial for troubleshooting and security.",

keywords = "n/a OA procedure",

author = "Herson Esquivel-Vargas and Marco Caselli and Andreas Peter",

note = "Funding Information: This work is partially funded by the Costa Rica Institute of Technology and the European Union{\textquoteright}s Horizon 2020 research and innovation program under Grant Agreement No. 830927. Publisher Copyright: {\textcopyright} 2021 IEEE.; 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2021, DSN ; Conference date: 21-06-2021 Through 24-06-2021",

year = "2021",

month = sep,

day = "2",

doi = "10.1109/DSN-S52858.2021.00029",

language = "English",

isbn = "978-1-6654-0252-1",

pages = "45--48",

booktitle = "2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks",

publisher = "IEEE",

address = "United States",

}

Esquivel-Vargas, H, Caselli, M & Peter, A 2021, BACGraph: Automatic Extraction of Object Relationships in the BACnet Protocol. in 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks: Supplemental Volume (DSN-S). IEEE, Piscataway, NJ, pp. 45-48, 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2021, Taipei, Taiwan, 21/06/21. https://doi.org/10.1109/DSN-S52858.2021.00029

BACGraph: Automatic Extraction of Object Relationships in the BACnet Protocol. / Esquivel-Vargas, Herson; Caselli, Marco; Peter, Andreas.
2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks: Supplemental Volume (DSN-S). Piscataway, NJ: IEEE, 2021. p. 45-48.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - BACGraph: Automatic Extraction of Object Relationships in the BACnet Protocol

AU - Esquivel-Vargas, Herson

AU - Caselli, Marco

AU - Peter, Andreas

N1 - Conference code: 51

PY - 2021/9/2

Y1 - 2021/9/2

N2 - This work presents BACGRAPH, a tool that extracts relationships among configuration parameters of Building Automation and Control Systems (BACSs) implemented using the BACnet protocol (ISO 16484-5). BACnet models these configuration parameters as object data structures comprised of multiple properties, some of which contain references to other objects. Given the regular exchange of objects among devices, we leverage these explicit references to build a graph of BACnet objects exclusively from network traffic. We tested BACGRAPH using traffic collected from a real building located at the University of Twente. After analyzing 66.8 hours of traffic, the resulting graph is comprised of 13,733 nodes and 3,169 edges. Such a graph improves the system visibility that BACS administrators have over their infrastructure, which is crucial for troubleshooting and security.

AB - This work presents BACGRAPH, a tool that extracts relationships among configuration parameters of Building Automation and Control Systems (BACSs) implemented using the BACnet protocol (ISO 16484-5). BACnet models these configuration parameters as object data structures comprised of multiple properties, some of which contain references to other objects. Given the regular exchange of objects among devices, we leverage these explicit references to build a graph of BACnet objects exclusively from network traffic. We tested BACGRAPH using traffic collected from a real building located at the University of Twente. After analyzing 66.8 hours of traffic, the resulting graph is comprised of 13,733 nodes and 3,169 edges. Such a graph improves the system visibility that BACS administrators have over their infrastructure, which is crucial for troubleshooting and security.

KW - n/a OA procedure

U2 - 10.1109/DSN-S52858.2021.00029

DO - 10.1109/DSN-S52858.2021.00029

M3 - Conference contribution

SN - 978-1-6654-0252-1

SP - 45

EP - 48

BT - 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks

PB - IEEE

CY - Piscataway, NJ

T2 - 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2021

Y2 - 21 June 2021 through 24 June 2021

ER -

BACGraph: Automatic Extraction of Object Relationships in the BACnet Protocol

Abstract

Conference

Keywords

UN SDGs

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Cite this