@inproceedings{outrata-etal-2026-command,
title = "Command-Line Obfuscation Detection in Real-World Telemetry under Extreme Class Imbalance",
author = "Outrata, Vojt{\v{e}}ch and
{\v{S}}t{\v{e}}p{\'a}nkov{\'a}, Barbora and
Pol{\'a}k, Michael Adam and
Kopp, Martin",
editor = "Mitkov, Ruslan and
Mu{\~n}oz, Rafael and
Lloret, Elena and
Ranasinghe, Tharindu and
Estevanell-Valladares, Ernesto L. and
Lamsiyah, Salima and
Montoyo, Andr{\'e}s and
Ezzini, Saad",
booktitle = "Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
month = jun,
year = "2026",
address = "Alicante, Spain",
publisher = "Department of Languages and Information Systems, University of Alicante",
url = "https://aclanthology.org/2026.nlpaics-1.8/",
pages = "74--87",
abstract = "To avoid detection by endpoint security tools, adversaries employ command-line obfuscation to alter syntax while preserving functionality. This paper proposes a scalable detection method specifically for command-line data, centered on a custom-trained, small transformer-based model optimized for low-latency inference across massive data streams. We demonstrate the method{'}s efficacy through a two-phase evaluation: first, by benchmarking the model on a controlled dataset simulating realistic command-line telemetry with extreme class imbalance, where it outperforms previous approaches. Second, we evaluate the model against multiple days of high-volume telemetry from diverse real-world environments. Our results show that this approach provides the high precision and computational efficiency required to handle large-scale command-line logs while effectively reducing analyst workload."
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<abstract>To avoid detection by endpoint security tools, adversaries employ command-line obfuscation to alter syntax while preserving functionality. This paper proposes a scalable detection method specifically for command-line data, centered on a custom-trained, small transformer-based model optimized for low-latency inference across massive data streams. We demonstrate the method’s efficacy through a two-phase evaluation: first, by benchmarking the model on a controlled dataset simulating realistic command-line telemetry with extreme class imbalance, where it outperforms previous approaches. Second, we evaluate the model against multiple days of high-volume telemetry from diverse real-world environments. Our results show that this approach provides the high precision and computational efficiency required to handle large-scale command-line logs while effectively reducing analyst workload.</abstract>
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%0 Conference Proceedings
%T Command-Line Obfuscation Detection in Real-World Telemetry under Extreme Class Imbalance
%A Outrata, Vojtěch
%A Štěpánková, Barbora
%A Polák, Michael Adam
%A Kopp, Martin
%Y Mitkov, Ruslan
%Y Muñoz, Rafael
%Y Lloret, Elena
%Y Ranasinghe, Tharindu
%Y Estevanell-Valladares, Ernesto L.
%Y Lamsiyah, Salima
%Y Montoyo, Andrés
%Y Ezzini, Saad
%S Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%D 2026
%8 June
%I Department of Languages and Information Systems, University of Alicante
%C Alicante, Spain
%F outrata-etal-2026-command
%X To avoid detection by endpoint security tools, adversaries employ command-line obfuscation to alter syntax while preserving functionality. This paper proposes a scalable detection method specifically for command-line data, centered on a custom-trained, small transformer-based model optimized for low-latency inference across massive data streams. We demonstrate the method’s efficacy through a two-phase evaluation: first, by benchmarking the model on a controlled dataset simulating realistic command-line telemetry with extreme class imbalance, where it outperforms previous approaches. Second, we evaluate the model against multiple days of high-volume telemetry from diverse real-world environments. Our results show that this approach provides the high precision and computational efficiency required to handle large-scale command-line logs while effectively reducing analyst workload.
%U https://aclanthology.org/2026.nlpaics-1.8/
%P 74-87
Markdown (Informal)
[Command-Line Obfuscation Detection in Real-World Telemetry under Extreme Class Imbalance](https://aclanthology.org/2026.nlpaics-1.8/) (Outrata et al., NLPAICS 2026)
ACL