Version 1
: Received: 13 September 2024 / Approved: 15 September 2024 / Online: 16 September 2024 (09:48:17 CEST)
How to cite:
Tobar-Rosero, O. A.; Roa Romero, O.; Rueda Carvajal, G. D.; Leal, A.; Botero, J. F.; Gutierrez, S. A.; Branch, J. W.; Zapata, G. D. GOOSE Secure: A Comprehensive Dataset for In-Depth Analysis of GOOSE Spoofing Attacks in Digital Substations. Preprints2024, 2024091170. https://doi.org/10.20944/preprints202409.1170.v1
Tobar-Rosero, O. A.; Roa Romero, O.; Rueda Carvajal, G. D.; Leal, A.; Botero, J. F.; Gutierrez, S. A.; Branch, J. W.; Zapata, G. D. GOOSE Secure: A Comprehensive Dataset for In-Depth Analysis of GOOSE Spoofing Attacks in Digital Substations. Preprints 2024, 2024091170. https://doi.org/10.20944/preprints202409.1170.v1
Tobar-Rosero, O. A.; Roa Romero, O.; Rueda Carvajal, G. D.; Leal, A.; Botero, J. F.; Gutierrez, S. A.; Branch, J. W.; Zapata, G. D. GOOSE Secure: A Comprehensive Dataset for In-Depth Analysis of GOOSE Spoofing Attacks in Digital Substations. Preprints2024, 2024091170. https://doi.org/10.20944/preprints202409.1170.v1
APA Style
Tobar-Rosero, O. A., Roa Romero, O., Rueda Carvajal, G. D., Leal, A., Botero, J. F., Gutierrez, S. A., Branch, J. W., & Zapata, G. D. (2024). GOOSE Secure: A Comprehensive Dataset for In-Depth Analysis of GOOSE Spoofing Attacks in Digital Substations. Preprints. https://doi.org/10.20944/preprints202409.1170.v1
Chicago/Turabian Style
Tobar-Rosero, O. A., John Willian Branch and Germán Darío Zapata. 2024 "GOOSE Secure: A Comprehensive Dataset for In-Depth Analysis of GOOSE Spoofing Attacks in Digital Substations" Preprints. https://doi.org/10.20944/preprints202409.1170.v1
Abstract
Cybersecurity in Critical Infrastructures, and in particular Digital Substations is a topic of wide interest for industry and academic community. One of the approaches widely used to support research on this field is the use of datasets (i.e., traffic samples collected during the operation of an infrastructure). However, generating datasets from operational electrical systems presents certain challenges: i) The generation of these datasets generally implies the operation under controlled or ideal conditions, which possible ignores the dynamics of real-world operations within a digital electrical substation; and ii) Captured data often contains sensitive information, posing challenges for publication within the research community. This paper introduces the development of a dataset for cybersecurity research, focusing on the analysis of GOOSE spoofing attacks, given the critical role of GOOSE protocol in executing operational and control actions within Digital Substations. The dataset exposes the real effect of the attack by executing unwanted operations or actions under various operational conditions, including both stable electrical system scenarios and situations where failures are present in the electrical system. The dataset was acquired from a laboratory testbed developed within a physical infrastructure that emulates the actual operation of a digital substation with two bays. Experiments allowed to observe relevant properties of the traffic associated to GOOSE protocol, and the actual vulnerability of a DS infrastructure regarding Spoofing Attacks.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
