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Authors: Lukáš Kotlaba ; Simona Buchovecká and Róbert Lórencz

Affiliation: Department of Information Security, Faculty of Information Technology, Czech Technical University in Prague, Czech Republic

Keyword(s): MS Active Directory, Machine Learning, Kerberoasting, Attack Detection, Cybersecurity.

Abstract: Active Directory is a prevalent technology used for managing identities in modern enterprises. As a variety of attacks exist against Active Directory environment, its security monitoring is crucial. This paper focuses on detection of one particular attack - Kerberoasting. The purpose of this attack is to gain access to service accounts’ credentials without the need for elevated access rights. The attack is nowadays typically detected using traditional ”signature-based” detection approaches. Those, however, often result in a high number of false alerts. In this paper, we adopt machine learning techniques, particularly several anomaly detection algorithms, for detection of Kerberoasting. The algorithms are evaluated on data from a real Active Directory environment and compared to the traditional detection approach, with a focus on reducing the number of false alerts.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Kotlaba, L., Buchovecká, S. and Lórencz, R. (2021). Active Directory Kerberoasting Attack: Detection using Machine Learning Techniques. In Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-491-6; ISSN 2184-4356, SciTePress, pages 376-383. DOI: 10.5220/0010202803760383

@conference{icissp21,
author={Lukáš Kotlaba and Simona Buchovecká and Róbert Lórencz},
title={Active Directory Kerberoasting Attack: Detection using Machine Learning Techniques},
booktitle={Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP},
year={2021},
pages={376-383},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010202803760383},
isbn={978-989-758-491-6},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP
TI - Active Directory Kerberoasting Attack: Detection using Machine Learning Techniques
SN - 978-989-758-491-6
IS - 2184-4356
AU - Kotlaba, L.
AU - Buchovecká, S.
AU - Lórencz, R.
PY - 2021
SP - 376
EP - 383
DO - 10.5220/0010202803760383
PB - SciTePress

- Science and Technology Publications, Lda.
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