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@inproceedings{2211857,
author = {Macák, Martin and Václavek, Radek and Kušniráková, Daša and Raimundas, Matulevičius and Bühnová, Barbora},
address = {New York, NY, USA},
booktitle = {Proceedings of the 17th International Conference on Availability, Reliability and Security},
doi = {http://dx.doi.org/10.1145/3538969.3544449},
keywords = {insider attack; insider detection; process mining; manufacturing},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York, NY, USA},
isbn = {978-1-4503-9670-7},
pages = {860-869},
publisher = {Association for Computing Machinery},
title = {Scenarios for Process-Aware Insider Attack Detection in Manufacturing},
url = {https://doi.org/10.1145/3538969.3544449},
year = {2022}
}
TY - JOUR
ID - 2211857
AU - Macák, Martin - Václavek, Radek - Kušniráková, Daša - Raimundas, Matulevičius - Bühnová, Barbora
PY - 2022
TI - Scenarios for Process-Aware Insider Attack Detection in Manufacturing
PB - Association for Computing Machinery
CY - New York, NY, USA
SN - 9781450396707
KW - insider attack
KW - insider detection
KW - process mining
KW - manufacturing
UR - https://doi.org/10.1145/3538969.3544449
N2 - Manufacturing production heavily depends on the processes that need to be followed during manufacturing. As there might be many reasons behind possible deviations from these processes, the deviations can also cover ongoing insider attacks, e.g., intended to perform sabotage or espionage on these infrastructures. Insider attacks can cause tremendous damage to a manufacturing company because an insider knows how to act inconspicuously, making insider attacks very hard to detect. In this paper, we examine the potential of process-mining methods for insider-attack detection in the context of manufacturing, which is a new and promising application context for process-aware methods. To this end, we present five manufacturing-related scenarios of insider threats identified in cooperation with a manufacturing company, where the process mining could be most helpful in the detection of their respective attack events. We describe these scenarios and demonstrate the utilization of process mining in this context, creating ground for further future research.
ER -
MACÁK, Martin, Radek VÁCLAVEK, Daša KUŠNIRÁKOVÁ, Matulevičius RAIMUNDAS and Barbora BÜHNOVÁ. Scenarios for Process-Aware Insider Attack Detection in Manufacturing. Online. In \textit{Proceedings of the 17th International Conference on Availability, Reliability and Security}. New York, NY, USA: Association for Computing Machinery, 2022, p.~860-869. ISBN~978-1-4503-9670-7. Available from: https://dx.doi.org/10.1145/3538969.3544449.
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